clocks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/efe/Documents/Research/RSMI-NE/rsmine/coarsegrainer/build_dataset.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import rsmine.coarsegrainer.cg_optimisers as cg_opt\n",
    "from rsmine.coarsegrainer.cg_utils import *\n",
    "from rsmine.coarsegrainer.analysis_utils import *\n",
    "sys.path.append(os.pardir)\n",
    "from rsmine.mi_estimator.MI_estimators import VBMI\n",
    "\n",
    "from examples.AB_prepare_dataset import edges, nodes, nodepos, order_8, G, prepare_data\n",
    "from analyse_codes import *\n",
    "\n",
    "examples_dir = '.' \n",
    "regions_data_dir = examples_dir + '/quasiperiodic_data/regions/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "cm = 1/2.54 # convert inches to centimeters\n",
    "fontsize = 7\n",
    "\n",
    "plt.rc('text', usetex=False)  # True\n",
    "params = {\n",
    "    #'text.latex.preamble': r'\\usepackage{tgheros}'    # helvetica font\n",
    "    #                   + r'\\usepackage{sansmath}'   # math-font matching  helvetica\n",
    "    #                   + r'\\sansmath'                # actually tell tex to use it!\n",
    "    #                   + r'\\usepackage{siunitx}'    # micro symbols\n",
    "    #                   + r'\\sisetup{detect-all}',    # force siunitx to use the fonts\n",
    "    'axes.grid': False,\n",
    "    'savefig.dpi': 400,  # to adjust notebook inline plot size\n",
    "    'axes.labelsize': fontsize,  # fontsize for x and y labels (was 10)\n",
    "    'axes.titlesize': fontsize,\n",
    "    'font.size': fontsize,\n",
    "    'legend.fontsize': fontsize,  # was 10\n",
    "    'xtick.labelsize': fontsize,\n",
    "    'ytick.labelsize': fontsize,\n",
    "    'text.usetex': False,\n",
    "    'font.family': 'Arial',\n",
    "    'figure.facecolor': 'white',\n",
    "}\n",
    "mpl.rcParams.update(params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def small_V_visualize(V_edgelist, edge_densities, edges=None, nodes=None, nodepos=None,\n",
    "                      n_rows=1, n_cols=1, title=\"\", figsize=(10.4, 6.8), node_size=1, width=3, cmap='bwr',\n",
    "                      layout_dict={\"pad\": 0.0, \"h_pad\": 0.08,\n",
    "                                   \"w_pad\": -10.08, \"rect\": (0, 0, 1, 1)},\n",
    "                      label_dict=None, edge_vmin=None, edge_vmax=None, savename=None, fontsize=3):\n",
    "    \"\"\"\n",
    "    Plot multicomponent data (filters,PCA components,....) on the V subgraph  (doesn't draw the full system).\n",
    "    Ensure that number of subplots is equal to the number of data components.\n",
    "    V_edgelist -- list of edge IDs in V\n",
    "    edge_densities -- mutlicomponent data to be plotted on V\n",
    "    \"\"\"\n",
    "    # ----- Prepare data, graph, .... --------------\n",
    "    R = nx.Graph()\n",
    "    elist = [(edges[e_ind, 0], edges[e_ind, 1], {\n",
    "              'colors': edge_densities[num, :]}) for num, e_ind in enumerate(V_edgelist)]\n",
    "    R.add_edges_from(elist)\n",
    "\n",
    "    num_vtx = np.shape(nodes)[0]\n",
    "    ndict = {nodes[i]: {'pos': (nodepos[i, 0], nodepos[i, 1])}\n",
    "             for i in range(num_vtx)}\n",
    "    nx.set_node_attributes(R, ndict)\n",
    "\n",
    "    ecolors = [R[u][v]['colors'] for u, v in R.edges()]\n",
    "    pos_V = nx.get_node_attributes(R, 'pos')\n",
    "\n",
    "    # ----- Plot subfigures ---------------\n",
    "    layout_dict = layout_dict\n",
    "    n_rows = n_rows\n",
    "    n_cols = n_cols\n",
    "    if n_rows*n_cols != edge_densities.shape[-1]:\n",
    "        print(\"WARNING: num of sublots and components do not match: \",\n",
    "              n_rows*n_cols, edge_densities.shape[-1])\n",
    "\n",
    "    fig = plt.figure(tight_layout=layout_dict, figsize=figsize)  # figure(1,)\n",
    "    axes_plots = []\n",
    "\n",
    "    for plot_ind in range(1, n_rows*n_cols+1):\n",
    "        try:\n",
    "            axes_plots.append(plt.subplot(n_rows, n_cols, plot_ind))\n",
    "            plt.gca().set_aspect('equal')\n",
    "            #plt.gca().set_title(title+str(plot_ind))\n",
    "            nx.draw(R, pos_V, node_color='black',\n",
    "                    node_size=node_size,\n",
    "                    edge_color=np.array(ecolors)[:, plot_ind-1],\n",
    "                    edge_cmap=plt.get_cmap(cmap),\n",
    "                    width=width, edge_vmin=edge_vmin, edge_vmax=edge_vmax)\n",
    "\n",
    "            if label_dict is not None:\n",
    "                nx.draw_networkx_edge_labels(G, pos_V, edge_labels=label_dict, font_size=fontsize)\n",
    "\n",
    "        except IndexError:\n",
    "            print(\"No data components for subplot\", plot_ind)\n",
    "\n",
    "    if savename:\n",
    "        plt.savefig(savename, bbox_inches='tight')\n",
    "        plt.close()\n",
    "\n",
    "    return R\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare and load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "case_dict = {'sigma2': {'8': {'case_no': 49, 'V_index': 389},\n",
    "                        '3': {'case_no': 50, 'V_index': 16830}},\n",
    "             'sigma4': {'8': {'case_no': None, 'V_index': 389},\n",
    "                        '3': {'case_no': None, 'V_index': None}}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#case_no = 50 # 49\n",
    "#V_index = 16830  # 393 #429 #404 #399 #V_indices[12] #V_indices[5] #389\n",
    "\n",
    "vertex_type = '3'\n",
    "scale = 'sigma2'\n",
    "\n",
    "case_no = case_dict[scale][vertex_type]['case_no'] \n",
    "V_index = case_dict[scale][vertex_type]['V_index']  \n",
    "\n",
    "EV_params, data_params, cGV_edges, cGE_edges = prepare_data(V_index)\n",
    "EV_params['case_no'] = case_no\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Highlight the $\\mathcal{V}, \\mathcal{E}$ subgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nodes/vertices: (26177,)\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "full_graph_visualize(cGV_edges[EV_params['case_no']] + cGE_edges[EV_params['case_no']], \n",
    "                        edges=edges, nodes=nodes, nodepos=nodepos, cmap='tab10', \n",
    "                        width=1, node_size=0.1)\n",
    "                        #ax=ax)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the MC samples from the disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapes:  (50000, 1575) (50000, 67, 1)\n",
      "len(Vs):  50000\n"
     ]
    }
   ],
   "source": [
    "######### Loading the (PRE-EXISTING) E,V data samples ##########\n",
    "samples = [10705]  # EV_params['sample_seeds']\n",
    "\n",
    "EV_dir = sample_data_dir = os.path.join('data', 'EandV')  #os.path.join(os.pardir, 'data', 'EandV') \n",
    "\n",
    "Es_list=[]\n",
    "Vs_list=[]\n",
    "\n",
    "for sample in samples:\n",
    "    Es_list.append(np.load(os.path.join(EV_dir,\"Es_%i_vi%i_c%i.npy\" % (sample,EV_params['V_index'],EV_params['case_no']))))\n",
    "    Vs_list.append(np.load(os.path.join(EV_dir,\"Vs_%i_vi%i_c%i.npy\" % (sample,EV_params['V_index'],EV_params['case_no']))))\n",
    "        \n",
    "Es = np.reshape(Es_list,newshape=(len(Es_list)*Es_list[0].shape[0],Es_list[0].shape[1]))\n",
    "Vs = np.reshape(Vs_list,newshape=(len(Vs_list)*Vs_list[0].shape[0],Vs_list[0].shape[1], 1)) # REMEMBER about the additional axis for Vs !!!!!\n",
    "print(\"shapes: \", Es.shape,Vs.shape)\n",
    "\n",
    "data_params['N_samples'] = len(Vs) \n",
    "print('len(Vs): ',len(Vs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10705] 10705\n",
      "Case: 50, shape Vs:  (50000, 67, 1)\n"
     ]
    }
   ],
   "source": [
    "Vs_dict = load_Vs(samples, [EV_params['case_no']],\n",
    "                  EV_params['V_index'], sample_data_dir)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimisation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimisation hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_no = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimates_statistics = {1: [], 2:[], 3:[], 4:[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'env_size': None, 'buffer_size': None, 'block_size': None, 'sample_no': 1070512121, 'sample_seeds': [10705, 12121], 'V_index': 16830, 'V_order': 0, 'case_no': 50, 'model': 'dimer_graph', 'lattice_type': 'networkx', 'L': 2048, 'N_samples': 50000, 'dimension': 2, 'll': None, 'size_V': 67, 'init_temperature': 0.3, 'min_temperature': 0.02, 'relaxation_rate': 0.005, 'Nq': None, 'conv_activation': 'softsign', 'hidden_dim': 16, 'h_embed': 1, 'nonlinearCG': [0], 'hidden_activations': 'swish', 'hidden_activations_L2_reg': 1e-05, 'batch_norm': False, 'logit_scale': inf, 'logit_minscale': 30, 'use_probs': False, 'use_logits': True, 'use_STE': True, 'layers': 2, 'embed_dim': 8, 'activation': 'swish', 'use_dropout': True, 'dropout_rate': 0.4, 'batch_size': 2000, 'iterations': 150, 'shuffle': 50000, 'learning_rate': 0.0005, 'run_no': 0, 'use_GPU': True}\n"
     ]
    }
   ],
   "source": [
    "# Linear CG ansatz\n",
    "\n",
    "# critic_params specify the MI estimator network -- not likely you need to touch that\n",
    "# CG_params specify the coarse-grainer\n",
    "# opt_params the settings of the training algorithm\n",
    "\n",
    "hidden_dim = 2 # Number of \"CG variables\"\n",
    "discrete_var = 1 #1 # \"1\" is a \"discrete\" CG variable, 0 is continuous.\n",
    "nonlinearCG_layers = [0] \n",
    "\n",
    "batch_size = 2000 # typically we use 400 or 800\n",
    "iterations = 150 #15 #300; #15 #40 #200 # number of epochs\n",
    "\n",
    "relaxation_rate = 5e-3 #8e-4 # 1e-5 # Gumbel-Softmax relaxation rate (from uniform to true categorical distribution)\n",
    "learning_rate = 5e-4 # 8e-4 typically around 1e-4, but can be played with, seems to work in the range [0.2-2. e-4]\n",
    "\n",
    "# None # pristine_f_l0 # Can be used to feed pre-trained filters.\n",
    "init_rule = None  # purified_filters\n",
    "\n",
    "##### Setting optimizer params ##########\n",
    "\n",
    "CG_params = {\n",
    "    'll' : EV_params['block_size'],\n",
    "    'size_V': Vs.shape[1],\n",
    "    'init_temperature': 0.3, #0.4, # Gumbel-Softmax initial temperature\n",
    "    'min_temperature': 0.02, #0.05, # Gumbel-Softmax min temperature\n",
    "    'relaxation_rate': relaxation_rate, # Gumbel-Softmax relaxation rate\n",
    "    'Nq': None, # Number of components of the d.o.f.s -- None means they will be set by default to a single component\n",
    "    'conv_activation': 'softsign', # If *not* using Gumbel-Softmax discretisation, this activation will be applied to the output of the coarse-grainer\n",
    "    'hidden_dim': hidden_dim, # Number of \"CG variables\"\n",
    "    'h_embed': discrete_var, #True is discrete, False is continuous\n",
    "    'nonlinearCG': nonlinearCG_layers, # [0] or None for standard linear CG coupling, a list of stacked dense layer sizes otherwise.\n",
    "    'hidden_activations': 'swish', #'relu', # activation function of the *in-between* dense-layers. The last one doesn't have any.\n",
    "    'hidden_activations_L2_reg': 1e-5, #1e-4, # L2 regularization of activations, useful with unbounded activations like relu\n",
    "    'batch_norm': False, # False \n",
    "    'logit_scale': np.inf, # 16\n",
    "    'logit_minscale': 30, #30, #3 #8,\n",
    "    'use_probs': False,\n",
    "    'use_logits': True, # True,\n",
    "    'use_STE': True, # False,\n",
    "}\n",
    "\n",
    "critic_params = {\n",
    "    'layers': 2, \n",
    "    'embed_dim': 8, # 8\n",
    "    'hidden_dim': 16, # 32\n",
    "    'activation': 'swish', #'relu',\n",
    "    'use_dropout': True,\n",
    "    'dropout_rate': 0.4, #0.4,\n",
    "}\n",
    "\n",
    "opt_params = {\n",
    "    \"batch_size\": batch_size, \n",
    "    \"iterations\": iterations,\n",
    "    \"shuffle\": len(Vs),\n",
    "    \"learning_rate\": learning_rate \n",
    "}\n",
    "\n",
    "run_params = {\n",
    "    \"run_no\": run_no, # to distinguish independent repetition of the experiment (other parameters being equal)\n",
    "    \"use_GPU\": True\n",
    "}\n",
    "\n",
    "all_params = {}\n",
    "all_params.update(EV_params)\n",
    "all_params.update(data_params)\n",
    "all_params.update(CG_params)\n",
    "all_params.update(critic_params)\n",
    "all_params.update(opt_params)\n",
    "all_params.update(run_params)\n",
    "print(all_params)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1 Pro\n",
      "\n",
      "systemMemory: 32.00 GB\n",
      "maxCacheSize: 10.67 GB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-10 18:18:24.955169: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2023-02-10 18:18:24.955318: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3a0abb1996247d9adfca246f1bf9250",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3750 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Len dat:  3750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-10 18:18:25.956012: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2023-02-10 18:18:25.956490: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_no += 1\n",
    "\n",
    "############## TRAIN (a single instance) ###############\n",
    "data_params['N_samples'] = len(Vs)\n",
    "\n",
    "estimates, Hs, filters, net = cg_opt.train_RSMI_optimiser(CG_params, critic_params, opt_params,\n",
    "                                                          data_params, E=Es, V=Vs,\n",
    "                                                          use_GPU=run_params['use_GPU'],\n",
    "                                                          init_rule=init_rule, use_notebook=True)\n",
    "\n",
    "if hidden_dim > 1:\n",
    "    fig = plt.figure(figsize=(8, 8))\n",
    "    ax = fig.add_subplot()\n",
    "    ax.scatter(Hs[-1, :, 0], Hs[-1, :, 1], s=10)\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Crudely visualize the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ii7lb7CbCE3eEweSUKlS6GFJyDCa4UHIMJoJlTrqd1WmoEAZYWbP9n6uaj2Zn7tQkiWeVmWymMo0nfyDEpaEDKOZUEnCfPGTNTXKPxfpY4DgrIlHGWGcRsyQkkkRP7W1FicTfgM7yUXmtHEQO+orF07AYEnIMJoJlSTMh8kBbSEJg/I+QtXTqRS3c+pbuigI7PKOku/pDlwUQUStu5vfUoCbzDDhgAchw2gmVOpoPpVswQLln5P5zw+KKuRnUi+JolNaqqMeDDNkn9qsyBRUVFXfzZIKAfnZMUx4CZUkzCfJLGcGODh/qkHVs7JCDbbMb98nDTPKY3vuXIuCCbFn12fk9w4CQ4MYeMGSkGECCg9ChtFMqNTRfFEWJsOMBVN9mWxLTLNrZ+U9zC5ixjJ2tFARMxYEOdua19cZloQMoxnXz51EIoGhoSEsXboU89yGVxSZfC/zTiVhUFIq2GVMc43ScZZGLNUPIiSvXZ+5nZwrmxGUVZNVvE6sdiUJ/+7v/g7Nzc146qmn0NLSgr1793orlWEYA1eScO/evXjrrbdQWVmJ0dFRrFu3Dg888ECRqsYw0wNXg/C6667D2NgYKisrUVZWhlrLeS+lJ99LvVM/YVDymvgJNHeaBt8pdoHjbg0z0rgRlGB6Fa/qqKtB+MEHH+DGG2/E6tWrceTIEcyYMQN33HEHAGBgYMBbDRhmmuNqEP7TP/1TsepREPI9PZ1KwqDgJ7LHlyQkxJpdhjq32kUQJaCkJJKwoqICX/nKV/D+++/jnnvuwcqVK9Hc3OytZIZhALi0jn7pS1/CX/3VX2FsbAx33nknHnnkkWLVi2GmDa4k4ZUrV9Dc3IyysjLccMMNmOk0sUmJ8BvAHRRViGynQ8caea7i7Ksc/VB8usrR9ewCrd26L+U9CGL0Ukn8hLFYDHv37sXly5fx0ksvYY6Pl62xsTH86Z/+KdatW4e2tjacPXsWPT09aGxsRFNTE3p7ez1fm2GChCtJuGfPHnR3d2PevHl47bXX8L3vfc9zwX19fZg1axbeeOMNPP/88/jGN76B/fv3480338Tk5CTWr1+PtrY2VBI28GQyiWQyafwfF3ksp4thhmynQzFuK0UvXRIfCEmo3ovLlwHYSyyvEi0oWolKSWJHu7q68MQTT6C3txdPPfUUHn30UW+lAlixYgWSySRSqRQSiQRmzpyJ5cuXo6amBtFoFPX19RgcHCTP7e7uRiwWM7a6ujrP9WAY3TgahM8//zzq6+vx7W9/G0uXLsXSpUuxZMkSDA0NeS64qqoKg4ODaGhowNe//nXccccdiMVixveRSASJPFm5duzYgZGREWMbHh72XA+G0Y2jQfjQQw/hN7/5DXbt2oW3334bb7/9Nk6cOIFXXnnFc8Hf/OY3cc8992BoaAgDAwP4zGc+Yxp0iUTCNChVqqurEY1GTRuQXx2IRjNbaJmczGwWjI9nNvLcc+fSG8WMGZnNZbWcUl6e3hw2Z0pRUZHZ3OBKHf30pz+NQ4cO4fXXX8cnP/lJHDhwwF1pCnPmzMFckUvhIx/5CObMmYNjx44hkUggHo9jaGgIDQ0Nnq/PMEHB1ZjdsmULdu/eja985Sv42te+hkceeQR33nmnp4IfeeQRbNq0Cf/6r/+K8fFx7N69G5cuXUJrayvGx8fR2dlJGmWsyCcJIxFn5wfpqZuDw8rbrsHoMhOaXbFu+zTI98DrhGRXp1VWVmLZsmUoKyvDRz/6UZT7iJ+KRCL4wQ9+kLO/vb3d8zUZJoi49hN+6lOfwp/8yZ/gW9/6Fua7XUeLYZgcXEnCf/mXf8Gvf/1rrFixAkeOHMHmzZuLVa+C4vRZEeiZ9Q4hbSrqBV2qo55SHjogKPdCpSTqaHV1NVasWAEAuPnmm72VyDCMiQDntsplRjk9byZsOWZI/CRlURv+u9/lP44wlBU6F0xQEjBTlCR2lGGYwuNKEq5duxZnz55FbW0tzp07h9mzZ2PBggXo6urCmjVrilVHhgk1riRhXV0d+vr68LOf/Qx9fX24+eab8dxzz+Fv/uZvilU/d+SJFl6wILNZIaM1AqkSjY5mNgvIqI5UKrNduJDfOEN0EBmBo+A1YsZDcI52qqoymxtc/dzeeecdLFu2DABwww034J133kF9fT0mAv0yxTB6caWOrlixAl/84hfR1NSEw4cP46abbsK+ffuM8DPt5HnklsVHSlyR4kJKHYdzhkgzunruiEVfecgx41arkMcHMR3+7NneznPV1Oeeew4vv/wyBgcH0d7ejj/+4z/G0NAQXnzxRW+lMwzjbhC+9dZb+NGPfoQrV67gyJEj6OnpwZ49e4pVN/eYFkVRcOiAnmLZOvJCSheHkpBUFj78kP6cjYcXNKv3QTmrRczJBpBpWxDfy73+flwNwgcffBDbtm3DokWLvJXGMEwOrgbhtddei8997nPFqgvDTEtcDcLa2lps3boVK1euRJl4Sb///vuLUjFP5NN9zp8vbT104Cc9mZqh9+LF/McRVnA7w7iVx0QmGlbVUUkQpzS5TfkvcTUIb7jhBgDAiRMnRKEeS2UYxsDRIPzf//1f3HLLLWhpaQnmwMuXriGg+JESpOlfvSAlliSEYcaPMz1s6UbU5Rvd4GgQ9vX14ZZbbslZj7CsrAzr16/3VjLDMAAcDsLt27cDAF544QWcPXsWV/K5AhiGcY2rd8LNmzfj3//937Fw4UKkUimUlZVNrSXR8jmXHPoJg6Jp+/Gh2U7qtVJHPYSxWKnONTW5+4K8PmFJJvW+9dZbOHr0aDDfCxlmiuJqEN500004f/781M0tk2UvNySGkdrdmqCs1EtKQrvHsAjtJ9voI72FHVaeEyqtLEfM2HD48GFcd911uPbaawGkDTNvv/22t5IZhgHgchD6SXvPMAyNo0G4detWPP300/j4xz+e8z44pQwzWRirManGBll/Qi8LojHAMUJXIl/nVd3PKrqIONmPn5BaLSuIU5gkRTXMSBdFtp+QYRj/OBqEV199NQDgvffewwsvvICxsTGkUim89957ePnll4taQVdkiTLDBG41UVXBNkX8FIE0dtiJcSurgRrgaWWYIcSeXeyoVbWsDDNBxKthxlWTH374YWzYsAEXL17EihUrME9G4DIM4xlXg3Du3Llob29HJBLBtm3bcPLkyWLVi2GmDa4XhDl8+DCuXLmC/v5+nD59ulj18kaW7mO8+KvGhqA4Ay2wXfKaQqiStgtd5VmY1StW6igbZtK4koS7d+9GIpHAV7/6VTz77LPYtWuXt1IZhjFwNXb/+q//Gj09PQCAH/7wh0WpUCExnrQFjgLRDSld7Cb1CquB7dPaKseMB6wMLdRUpiBHzLhcTtPA1SAsLy9Hf38/brzxRmNtwsWLF3srmWEYAC4H4QcffIDu7m7j/yk3i4JhAoirQfjQQw+ZEj09//zzBa+QL7J0GCMZa4GNDboh1VE7h52Y9k2qeeoFrQw8hC7rJ2KGWsY8yOrorFneznM0CHt6evDjH/8Y//Zv/4ZXXnkFADA5OYmf//zneOihh7yVzDAMAIeD8I477sD8+fNx+vRpfOELXwCQfj/82te+VtTKuSavi8JioqqC1xfrUkMuOGInCUXcp682EiLYz0q9lItCti0oiZhVipoGf86cOfjYxz6Gj33sY95KYRgmLwHUvBkmXAQ4PoEgnzp60tnM+qAYA0jDjF3EjNBDSXXU6dJ2hBXGbi0+q0wolGHG6XWnIl5THgbkZ8cw4SVckjArasR4uXcYBRKU/FWkJEwmrU8ipJgRseI0nla1lghRZTeDysqFQWVbk9cLilaiEkhJ2NnZiXXr1qGxsRG9vb3o6elBY2Mjmpqa0Nvbq7NqDFMytEnCgwcP4n/+53/wxhtv4OzZs/iHf/gH7NmzB2+++SYmJyexfv16tLW1oTIofgOG8Yi2Qdjf34/FixfjrrvuwujoKJ588kkcOnQINUJHqa+vx+DgIFauXJlzbjKZRFJRv+Iyh0yWbmRoT+qUeYscM07tE7ohVUC70BXxMFO7wjBcOU2uo+pbonPt4sat+jRsa1F4NSZpU0fff/99HD9+HPv27UNnZydWrlyJmJLvIBKJIJEn3Ky7uxuxWMzY6urqSlVthik42iTh/PnzsWrVKlRUVKCxsRFz5841DbpEImEalCo7duzA1q1bjf/j8Xh6IGYlibnqKvFBXTvDwggRFGMA6Y1wOKlXZcEC8cHL2oYiPMRPn1HZUaQ0CeLk3pLkmCkka9euNZJEHT9+HNdffz2OHTuGRCKBeDyOoaEhNDQ0kOdWV1cjGo2aNoYJKtqeN3fddRdeffVVrFmzBqlUCt/97ndx8uRJtLa2Ynx8HJ2dne6NMllPdMMZbCclwoBdqn/Rl6pANCSRF0ko3t3tTrW6hVYLwgTFXaRSkgVhCklZWRmefvpp077Gxka0t7drqhHD6CEgb0EME14C+PprQZY93Jha4lAdDaIKZODQMKMaUgx13UvWY6F72algVt4P6lVeekGC6B4OnIuCYZg04ZKEWU90w/BgF1cpCMrTl5QudkuYE5LQ8AB5iVJwKAmt+nRWlSw3Yy2SkrC6IiCREwqBc1EwDJOGByHDaCZc6miW08owzKiqmkXsaKhXZRJ6qHqYYRjxsjCjsOrYRcxYzpIyfJuZ2b2GeuvFd6mZkqTBZxim8IRLEmY9ioyIDFUSypAR4kkblFkUpDfC7jFMpAIzYmudopYhPtsJLEshe/Gi+JCRhMY9CGB+C5aEDBNQeBAyjGbCpY5m+QMNdVS1DljoT0HRgEh/lJ1xRbRbbePcueLDeQ8B7sLAZddnlt8b6mgGI8DcLiB9CkIFpDuBJSHDaCZckjALt9MMgzKpl8Su8sJqoB5mPLnP5z0cgKI8qCLYoRXCslqEJAwyXiOugvyzY5hQwIOQYTQTLnU0K48K+aIs1SjCQGMXAz1V8JQoQFhIVPXQ0C6dWqRUfUs4Ge3qYvk9kZTZ8BMG8N3AaQ7lbILXUoYJGeGShFmPItIwYxEWE8CHbwa76VpC2qn2FENTIIJmyb5Q846KC3kJOzUgyjWKKEVeINL65B2eysQwAYUHIcNoJlzqaBaZmdsKXt+ePWCydxRbu7JrF+HXM9QnQkUn1VH1GuJkX+oooXIafebrwg5RDU0FUEc5gJthAkq4JGF2ujSXL/dBcVGQk4/tUsUJEaN2iWG4IsQeKQlVySGsOnYToS3tRVYRM6WwkqlLBV++7PtyXoU3S0KG0QwPQobRTLjU0WzjhMsA4ULbAkpqmLGDWJXJWJ+QQK07OatInGxnD7LMVkCogEY11ZthEeXkCyLbgB+8atAsCRlGM+GShNlQktDiqVpoSVisNfbsFuUlEUYV9WltJQltVwNWo2e8YmW1UStQLCPNrFnFua5LWBIyjGZ4EDKMZsKtjhJTZazU0UKnPPQa0GsHqebaTesWqqRqcHG9wLFasOpjs8BSkyQsPkYz1PvjSf92QIFvEEfMMExACbckvHAhd5/F46rQYaXFyt5GSheHxgvV3mFY6IloG1VIxOPEceLkMpsZVJbGLiJEyWiGeqIUjwWIajHhOvtxcWBJyDCaCZckzHZJfPBB7jEWL36FdikU2BfsDxHkqb5qGWsAEioAWXfVLSFElp2nwvJ1zkpMlnpSbwHgSb0ME1B4EDKMZsKljmbPq0kkXJ0eFBcFqanZ5ZgReqjpXKm+E2ohWXfV0iSzt9lojZbqKNEQw/aj1ikgLgqvhjiWhAyjmXBJwuwnJmXStjB3B8VFQUpCOzEunvqmOklnuVNJqLpBpOXGj/2ECJgoREiqYwosCb0KbO2S8MyZM5grlgfq6elBY2Mjmpqa0Nvbq7lmDFMatEvC7du3Y3R0FGNjY9i5cyfefPNNTE5OYv369Whra0Ol11U2GCYgaB2EAwMDWLBgAWpra3H06FEsX74cNSJ3SX19PQYHB7Fy5cqc85LJJJKKISIuQzqy1SpqKpOFjmhn23BLsfyEpHvNbh4WkfzXatIz2U1U5uAL1sVaQkT5GEWoqmqxLFxTZEFKbero6Ogourq6sGvXLgDpgRSLxYzvI5EIEnmsm93d3YjFYsZWV1dXiiozTFHQJgkff/xxfPnLXzYkXzQaNQ26RCJhGpQqO3bswNatW43/4/E4PRCtZlEQFNowU6wHuKfJx9RTXxpmCKOObTCJwykYlhkpiC+NctVGFstaM0ViR7UNwp/+9KcYGBjAM888g9OnT6OjowMnTpxAIpFAKpXC0NAQGhoayHOrq6tRXVIzGsMUD22D8LXXXjM+L1myBD/5yU+wb98+tLa2Ynx8HJ2dnWyUYaYF2q2jAHDixAkAQHt7O9rb271fKFu9oSwtFi/jhU7mVdL3fjsdVRhBTLYQqf4TeritOiqsTnbFWvYpMZXJ6DPVGWqX2NgrDicmO8WrcqbdT8gw050pIQkLRraBwZiNqlCsOESCYhlmSLxYayx8MqR7hVgQxg7LucZE6I9x2QDGjnqFJSHDaIYHIcNoJlzqaLYlhMrfbmFxLfRk7mJpUaSxwy7HDGVpkf1DGD5IwwwRMWOnBVt+T3xJllus0KMCG2Z4VSaGCSjhkoTZUo6KmLGQGIWWhMVyc3qqJ2WEkLGjTnPMqJrGvHkA7NcntFzzkRDphlBW71Ox0uCzJGQYBuBByDDaCZc6mg0129wi+qLQqzKVND7YYcSMyfAh9Vqb5L8G6nEOfWyWE/6JOhsqPLU+YaEpsMGH0+AzTEAJlyTMftGnLBglnH1R4Pd+A1KYO5SEJixiR+3cKxOT6UrYdaelJCTuj1FN9btiScICr09YWeFtLhxLQobRDA9ChtFMuNTRbJWLipixUG0K7SeUaVgKjacMANRUJulHJfqEVDMV9U12rS8XnpUKrV64WHPCCv2+4NGyx5KQYTQTLkmY/SSiEkVZPLoLPam3WJLQU7Y1qt0yYob4TjX+GO1QJBKlZFBYRtRYTer1MZXJJDittJtC3yCWhAwTTHgQMoxmwqWOZr/AUwHcFi/5hTbMFMu9RWo9dhYSEeFiOkyqg8S5avC5EVii9J3TvrIL8M7GaJtaJ5ehR6ZsjOecHlgAPFqpWBIyjGbCJQmzoebRWIinQseOljQNvp3YFd+bQj6lJYqIA1V3GQJQKUOe6kvaFyl2VMyySlNKSejRsseSkGE0Ey5JmK2TU5KwhO+ExQpTtZwomw8hYkxCRfoZiBhKVYobXabslK/bdq9BbtPgG33mY0EYkyS0otCSkF0UDBNMeBAyjGbCpY5mqwNUWIdF9EWhU5nMmVPY60nsVsa2glwue/78nONUddRoh7JT5g22y1BP9qmFocXQPFWd26VhZsEChwcW2nJmek9wHuXDkpBhNBMuSZgNZWmxSIFWaBdFnuUVfePWAa5iEirSukL0iSrBDIGhaBFSiHrKrSpjNok0/IakVg0zLtPWOTbMFDV2lCUhwwQGHoQMo5lwqaPZaguljlroT4VWR4s1lYk0zNhZlUTfmFxuly8jd2cauwWYpA3CU8SMRccY11PVUZcOV8LORHJ5NPNbKEi2GY/hQywJGUYz4ZKEM2aYn9pUaImn3BDeMD3wZVTKZf/XJYW5QzFum96eKMMwdCiRNUnRDruHPymgpc/DKv2+eu9cpreorXV2nOrBKmzeNXewJGQYzfAgZBjNhEsdnTXLHC1xkjjGQrUptGHGpBpffXX67wn/1yWDou2MF6Ld1eWKk1GGu9hM6jXm1Cp9N3Ex9zjHdZU3yWpdRNWo5jKyxWnEjEyxAwAObTnWmBrr3LfJkpBhNBMuSVhVhYULlf8pSUjY26UBpdBTmUwP8GuuSf894f+6pMS2C12RIkY1eEhfh03EDCUJ5WUcR6eoSMMMETFj2H7UsCCXU5mcxuzG464uaw+7KBgmmPAgZBjNhEsdrazERz5icwxhhJATrAud/Nc0cXvRotziC2kIsvOlSSOI6hyTKh+RzUydbG9og0oZ0qhh19+khiYvTkw1I+1LLv2ETlXkCxdcXdYek9qcq2rnQ5skTCaTuPfee7F+/Xo0NTXh8OHD6OnpQWNjI5qamtDb26uragxTUrRJwr179+LGG2/Eiy++iKGhITzwwAO4cOEC3nzzTUxOTmL9+vVoa2tDJWE0SCaTSCov9XH5hl1RYW+eJl7y5Yt8oV/UKyeVp6EwzJiiaCbFP4qp3AnkVCY7M7403FBRREQfq8LRuLTSd7KvPOWYkSEtv/tdzlfkkoEuZ1vb/Qbk/R4ZcXVZe0wS27kk1DYI77vvPpQJFWliYgJDQ0NYv349asSvtL6+HoODg1i5cmXOud3d3XjsscdKWl+GKRba1NFIJIKamhqcO3cO999/P1566SXElFmwkUgECWpBFwA7duzAyMiIsQ0PD5eq2gxTcLQaZo4fP46NGzeis7MTsVjMNOgSiYRpUKpUV1ejmnqDz6eOzp2b+WzhJyz4i7pqeBAOTJMPq0r4Dn/l7rK+jBeqM1QGUBM6oKqhGl2m6NIWGfRNWAZwE3UuhGHGbiqTsJEV/H4nRzMRQJEIvSgYhbZB+O677+Luu+/G3r17sWbNGoyOjuLYsWNIJBJIpVIYGhpCQ0ODruoxTMnQNgi7urqQSCSwfft2AEBtbS26urrQ2tqK8fFxdHZ2kkYZO0jzNJnTPYM0s58967q4DKrFRdrvz5/P7KurA5BlNJjtTBIaUUCncosysIvWkBYSVTpbnENOZVL6UV7OTkiRRUjfDRH6Y5SlWp8sjE6qG0gai667zrpOS5ak/1LrBflBbc7ChcDQkLPztA3C73znO/jOd76Ts7+9vV1DbRhGHxwxwzCaCVfEDPL4iCKRzGdCf5Lqna+pTGrk+C9/mf6rvvlff72pLADAdXXuLi3UUVLFcxoxQzlD1UqJz2oZhjFJOU5ezlP+XKlzEkYycnkIi7apETuyaWWn37MsXrwZFNwvrNq8rr3WuTrKkpBhNBMuSThzplkSyse5arMmbODygezSEm5Gvu0DGQuP+mgU35ue9HXOJKGcBSUxCZBrr03/tUvtJo1capiI1BBUv4nYp/aFLG+iKteVYddnZMSM7ARCjFZCGGRUUSwLUScBC++KqoD8Shq4Tp+2rJNQSshVEky/H6u1DZWbUCOqqkrWxYstq2CCJSHDaIYHIcNoJlzqaGWlOd2dVNFUdVQ10gikZuFrkR7VOSWtBUoSk7Fr6kxVAmBWYS3IPsw0if6GG9J/lcobxgrV7ylVOipMRI3WFqqpWobUHlV1S37vKYBbqr/UjHlqHW5ZP7UjfpP+Q6p956z0SGDZsvTfw4fzf5e+jsVFlIIXE0kLli61rIIJloQMo5lwScLZsw07BYDMk1YNoyFyscgnPZnKRN0pH3XqE/mE+Cvf9gFABpQrT3NpNDC9+Fs8LtXjjMPE9UxTmeSjWzE4ydhIzFTEhDTMqOJM7lPVB6E1qOGk8rMqYCwnQJPzkRSyVA5Tn0jtQTUWyc+/93uZfUISqrfCMGA5lISvvJL7nUnrOFMPAKh5X62f+Cs1EABLiVliJolqA0tChtFMqCTh5dEZ5ncE+WhUbfwWafBJSXjzzZnPP/95+u9NNxm7lsrHmCF+kHkMKi+A0n9veuqLyppcEMK6rsauG20SO03vlStWpP8q7TIkZyzztDaknppsU0oY1eMtbP7qK5kUXOqpsq8oMz9+//eNj8b3N96Y+V42QLynmqSGlNSq9iLrp3ZKf+5lja+picsKsn9MSpG4fyZJeDHdjhtPKfsGZ+bUpUFcRw0VVutlB0tChtEMD0KG0Uyo1NFEArh6nmK1kCqiGlYhoy4UfUuqJaonw1Ab/+APMjtPimzCSsqNm+VsK1UdlTqYolK9/Ub6r0ndEXqoot0CuCanWEO1EaqnaWLw4lvSf5VoEqmh4nxGLTSsOe8rVgZRv/dOZ869VuxT42ilNqgGosj+USNmDK1WUeGvvCH3rcgcKNVRoeea2i/XTFR1dHmv1FcDgaqh3iK6wqQXSl3+ncyuyGQ6amjePGXSuNBRTdOgytI34WZ1elyFKERRuW8W1VONVcuXOl/TnCUhw2gmVJLw0iWYH0dS7KhS6j0RYb96tbFLvscrVufMhE/lOLz7bvrvH/6hsetWKQlUQ8Ktt6b/KhJYShHjaQ0gOTuddqOxUWnEzFUAgI9+NLPLEACiXJMbRtbvncyjXhaPC0rdpSRUjRbiaa56La4VEkHdFytP52kYH88EOsjmquGxRp2Vyl8SBhSTpUKKciHtVqshtEItSS3OuHzKzn+Q/qD0u5S6RlsBHDsmPpxUKiX6Z5E6Q0YELCxZokhC0Rcm78qq9L1YfbWyb/bqnIJXi9/A228rx731FpzCkpBhNMODkGE0Eyp19MMPYXZwSRVIVUflS7uiTkgXm+ouM7RQVS+U+oaiFq2WxanWEqFmfXApo9tILVD1E0pVTlWpUJUuT1Vby345ZCp3jpJ6Z/hKOtqlrjyzBFVGHVUuLOuuhtsIB90pxQ+2XBgyTBOchYNwdDSjjlJpYgy1WtGvDcON4gxMzUmr4WWin24lZnRJzR8AFtWmDTkTt6wy9snbUvmro8a+VauEheesYi0SN/JWNcJHGM5MPuUPV5jrCxgdeaua6l+qo4qR6A/Emo+/+IVyY4Z+kduoPLAkZBjNhEoSjo4Cl8czT6NZUrRR0yOUuM3kb9N/1Qm3hp1FDecQq+1OLMoYDRZLa7jyCP2dMGCo0STya3XWgZSEJrfFpfQ/piiaE2dNdYldyHwljRF1ioQ17EHzlNjUXxBPZiG9L6gGBdFnptkRoqKqEYZKQ2GEdipGGOM6ygkytLNWdLIadouz6U67rP4yRef96peZXYaHQBHjv7dOSML9lzMHCh/GEmJGiSl6Sfgmrqhhp0KDWmQSVem2vR/PxOrWnjkCAJicVFwov/41nMKSkGE0w4OQYTQTKnV0YiJrio1UQ9XIbKkfKYaUCaGOqS/lRpC0anAR11N9aJRaJufNqkYLavKr3GfSloWKZgrSlo0S35UpaWKMObofyRRQLVYEGpud8YNVUmuBi4JNX8m+Ig5X+5Zqj4w4mojOzT2OSBwsJ+ua2noq/aXadymkI3rUQBhDlbycUT2rR0XeeTUyW6jX5uUHqrKrZFRiXIkKkjl1TPUT98AUuC5WlzL14wcfwCksCRlGM6GShJOTWZJQPupUt4V8NCuPQfnUVZ++UipOlGcMPTPEddQpPZREkNE2amI36RmgpKPJLE7k/aRFZhpjmUa1IqITLo1nKhCTj2n1OHE908wf8b3pqS4qrdadyn0qu5TM56k00ri2CE8xrXYgylc9KbJcVfoYXaFOTaOmMIn+NPWxKMN070Rd1N+PrKepreJ6pqKocsk5XjQsCRlGMzwIGUYzoVJHcwwzUt+gdCfl5X1iwnw4kFFf1OvNEAeo+4xLq4aE/JP3TWWQ1RP/UEl1x8bLcvYZZamJcYUeNT55Vc4+Suc2aVN2qzsJKDVcnqr2j9EO5brG92KfSR0V9SNSAZlUZOO6amHUooninYDqT1NTiRXAqN+FvNCYuqJTklgam1zTnIYlIcNoJlSSMJXKemrJfyjxQ2QEoySh+kCrJowW1BKKVCYyp9JRPp5N+4R0kNdVr2UINlV0ZB1v+kc+3pWyTA9tShKKyti5XOSpqmAwjlNEkXEdykBCXF8eT0pCtVKyjerJ4gbZlSH/MWXaTxHHiUaa7qfapxKbPDemejg+kmGYosCDkGE0Eyp1tKyMVh1I3UZRu6RqoWp0pH2CUMsMdXTM2eKGqrpDpLsxdpr2UeplnrqpB5qmI1Eni3OsVGX1QtT6jWqxss7q9Yx2WOTLt1UVBaRBLN8BWRei+tMpJiMR8RuQ5ZrqbHmzsqroqjYMwxScUEnCnCc6KWryoz7xyAc38QSljrN60KplUGZ42YgyUNYXh2WJp/BEKncfJaZIt44N8jBVsltKNOVAo85in5WRRz2ejNixM8xQklBASTjyniD3OBOU1GNJyDDBgQchw2gmVOpoWVmWFiB1C4fqaPa1ciAiZgztRNlJqYhyH+WyJAt2aTygDDMm7xUVwUEZGSz6ilIHKRcZWXVFzzO+F1Ytk+pNdIrjrpD3gIgKogLiK4lIJVso3yZluLK1dimXdHwkwzBFIVSSEMh6GlkZGYjvnL6820WOWEFJQtM1ZIHE49/CAm8+Xj6Z1QOlyCIsKXaGEUoDoCShZV9QItOhb4I6lTzewjDjNGLGFuo4UUG3vwXjkt5OKx6Tk5N48MEHsXbtWnzqU5/COZsFHxkm6Ey5Qbhv3z7MnDkThw4dwgMPPIDu7u6cY5LJJOLxuGljmKAy5dTRQ4cOoa2tDQCwYcMGPP744znHdHd347HHHnN+UerNntA91V1SBaJUQDWQ2DhHOVkWp54r7SLqlBp5iq3hQRwg1R31usa5quFFFGLrqnKqWhGB67IdVLA2oRlbWnDUKVqVolOoe0FF55jSFxDRUGRAvAWUf9I0DWoGMSdKVNB0H0nLXp4yHR9ZIuLxOGKxdIKiSCSCRCKRc8yOHTuwdetW4/+RkREsXrwYH34Yh3r4LJlnQt0pEwMp6QdGR+PZu4x7qwrZlDj34sXMTvl9Sinjww/TO6kco2pV5LmmfbLOasFin5T4anoN2Zy4ulP8Gi5OKPWUo0UdSeJ6V65kpoLI68g2AEBcVPDy5cw+WWe1WOq1U+4z2oVM/8XL0yePKW2tFBdMVOZqN+r9kT/4eErZKSujzmAw6p6ZNSPbk6jKlFEtzlXbKKulDsIxyN9A5qEXFzfh8hXlXNHwlBMraWqK8cgjj6ReeumlVCqVSl24cCG1evVq23OGh4dTAHjjbcptw8PDtr/fKScJm5ub0d/fj7vvvht9fX247bbbbM9ZuHAhhoeHkUqlsHjxYgwPDyNK5SIMAPF4HHV1ddwGzfhtQyqVQiKRwEJ1gdo8TLlBeM8996Cvrw9r165FVVUVvv/979ueU15ejkWLFhnqWjQaDezNl3AbpgZ+2iBfq+yYcoNwxowZ2LNnj+5qMEzJmHIuCoaZboRqEFZXV+Nv//ZvUa2arQMGt2FqUMo2lKVSLiJNGYYpOKGShAwTRHgQMoxmeBAyjGZCMQiDPPMimUzi3nvvxfr169HU1ITDhw+jp6cHjY2NaGpqQm9vr+4qOubMmTOYOze9NmEQ29DZ2Yl169ahsbERvb29pWuDrxizKcIPfvCD1JYtW1KpVCr1/e9/P7V161bNNXLO3//936d27tyZSqVSqWPHjqWamppSDQ0NqUQikRoZGUmtXLkyNTo6qrmWzvjCF76Qmj17dmp0dDRwbRgYGEh99rOfTaVSqdSZM2dSX//610vWhlBIwuyZFwMDA5pr5Jz77rsPHR0dAICJiQkMDQ1h+fLlqKmpQTQaRX19PQYHBzXX0p6BgQEsWLAAtbW1OHr0aODa0N/fj8WLF+Ouu+7C5z//eXzyk58sWRtCMQidzLyYqkQiEdTU1ODcuXO4//778dJLL5nCnYLQntHRUXR1dWHXrl0AzPcDCEYb3n//fRw/fhz79u1DZ2cnVq5cWbI2TLmwNS9Eo1GjgxKJhOOYvanC8ePHsXHjRnR2diIWi5ludhDa8/jjj+PLX/4yasQqtur9AILRhvnz52PVqlWoqKhAY2Mj5s6dW7I2hEISypkXABzPvJgqvPvuu7j77rvx3HPP4e6770ZDQwOOHTuGRCKBeDyOoaEhNDQ06K6mJT/96U/xzDPPoKWlBadPn0ZHR0fg2rB27Vq8/PLLANIPxeuvv75kbQiFJPQy82Kq0NXVhUQige3btwMAamtr0dXVhdbWVoyPj6OzsxOV1PprU4jXXnvN+LxkyRL85Cc/wb59+wLVhrvuuguvvvoq1qxZg1Qqhe9+97s4efJkSdrAYWsMo5lQqKMME2R4EDKMZngQMoxmeBAyjGZ4EDKMZngQMoxmeBAyjGZ4EDIAgJdffhl79+5FMpnEP/7jP+quzrSCnfWMiRMnTuCBBx7Aq6++qrsq04ZQhK0x7jl+/Dg2bdqE8vJyRKNRtLW14fz58zh9+jT++7//G08++STuvfdefOlLX8Lo6CjmzZuHPXv2TPlA7CDC6ug0pb+/H7fddhsOHjyILVu2GFmmOzo6cOutt2Lbtm3Ytm0btm/fjoMHD2Ljxo144oknNNc6nPAgnKY8+OCDmDlzJj7xiU9g7969GB4ezjnmyJEjeOyxx9DS0oJnnnkGp06d0lDT8MODcJryox/9CLfffjsOHjyINWvWoNxYVrock2LdseXLl+Ob3/wmXn31VTz99NP49Kc/rbPKoYUNM9MU+U5YVVWF8vJyfO5zn8Nvf/tbdHR0oKmpCffccw/uu+8+/MVf/AWuXLmCyclJfO9738Py5ct1Vz108CBkGM2wOsowmuFByDCa4UHIMJrhQcgwmuFByDCa4UHIMJrhQcgwmuFByDCa4UHIMJrhQcgwmvn/xx354QIz9U0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "# plot the filters (flattened to a 1D array) with time dimension going up:\n",
    "if len(nonlinearCG_layers) == 1:\n",
    "    heat = np.array([filters[:, i, 0]\n",
    "                    for i in range(filters.shape[1])]).transpose()\n",
    "    plt.imshow(heat, cmap='bwr', interpolation='nearest',\n",
    "               aspect=1, origin=\"lower\", vmin=-0.2, vmax=0.2)\n",
    "else:\n",
    "    heat = np.array([filters[i][0][:, 0] for i in range(len(filters))])\n",
    "    plt.imshow(heat, cmap='bwr', interpolation='nearest',\n",
    "               aspect=2, origin=\"lower\", vmin=-0.2, vmax=0.2)\n",
    "plt.xlabel('site')\n",
    "plt.ylabel('training step')\n",
    "plt.show()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Plot the mutual information (MI) as a function of training step:\n",
    "plt.figure()\n",
    "\n",
    "#best_estimate_id = np.nonzero(estimates>1e-1)[0][-1]\n",
    "\n",
    "plt.plot(estimates)\n",
    "#plt.axvline(best_estimate_id, color='gray', alpha=0.3)\n",
    "plt.xlabel('training step')\n",
    "plt.ylabel(r'$I_\\Lambda(\\mathcal{H}:\\mathcal{E})$')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse the results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the trained networks (coarse graining map)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The nice built-in visualization functions expect this \"dictionary of cases\" format of inputs.\n",
    "%matplotlib inline\n",
    "\n",
    "scale_fac = 2*int(np.ceil(np.cbrt(len(cGV_edges[EV_params['case_no']]))))\n",
    "if len(nonlinearCG_layers) > 1:\n",
    "    if nonlinearCG_layers[0] > 20:\n",
    "        skip = nonlinearCG_layers[0] // 20\n",
    "        num_cols = 20\n",
    "    else:\n",
    "        skip = 1\n",
    "        num_cols = nonlinearCG_layers[0]\n",
    "\n",
    "    small_V_visualize(cGV_edges[EV_params['case_no']], filters[-1][0][:, ::skip], edges=edges, nodes=nodes, nodepos=nodepos,\n",
    "                      n_rows=1, n_cols=num_cols, figsize=(num_cols*scale_fac, 3*scale_fac), title=\"hidden\", width=16,)\n",
    "else:\n",
    "    small_V_visualize(cGV_edges[EV_params['case_no']], filters[-1], edges=edges, nodes=nodes, nodepos=nodepos,\n",
    "                      n_rows=1, n_cols=hidden_dim, figsize=(hidden_dim*scale_fac, 3*scale_fac), title=\"hidden\", width=16)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save a figure for the trained filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 3.93701x78.7402 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_V_visualize(cGV_edges[EV_params['case_no']], filters[-1], edges=edges, nodes=nodes, nodepos=nodepos,\n",
    "                  n_rows=1, n_cols=hidden_dim, figsize=(7*cm, 1.3*cm), title=\"hidden\", width=1.33, node_size=0,\n",
    "                  savename=scale+'_'+vertex_type+'v_filters.pdf')\n",
    "\n",
    "\n",
    "a = np.array([[-np.max(filters[-1]), np.max(filters[-1])]])\n",
    "plt.figure(figsize=(0.1*cm, 2*cm))\n",
    "img = plt.imshow(a, cmap=\"bwr\")\n",
    "plt.gca().set_visible(False)\n",
    "cax = plt.axes([0.1, 0.1, 0.6, 0.8])\n",
    "plt.colorbar(orientation=\"vertical\", cax=cax,)\n",
    "cax.yaxis.set_ticks_position('right')\n",
    "\n",
    "#plt.savefig(\"colorbar.pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "optional"
    ]
   },
   "source": [
    "### Purify the filters by generating rotated components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x359c42170>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x3000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle = np.pi/2\n",
    "\n",
    "fid = 2\n",
    "\n",
    "f = []\n",
    "for i in range(2):\n",
    "    subarr_pos = rotate_edges(cGV_edges[EV_params['case_no']], i*angle, nodepos, edges, target_vertex=V_index)\n",
    "    f.append(filters[-1, subarr_pos, fid])\n",
    "\n",
    "\n",
    "for i in range(2):\n",
    "    subarr_pos = rotate_edges(cGV_edges[EV_params['case_no']], 5*np.pi/4+i*angle, nodepos, edges, target_vertex=V_index)\n",
    "    f.append(filters[-1, subarr_pos, fid])\n",
    "\n",
    "purified_filters = np.array(f).transpose()\n",
    "\n",
    "small_V_visualize(cGV_edges[EV_params['case_no']], purified_filters, edges=edges, nodes=nodes, nodepos=nodepos,\n",
    "                  n_rows=1, n_cols=4, figsize=(hidden_dim*scale_fac, 3*scale_fac),\n",
    "                  title=\"hidden\", width=16)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of pre-activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_prods = dot_prod_V(Vs_dict[case_no][:, :, 0],\n",
    "                        None, filters[-1])\n",
    "\n",
    "fig = plt.figure(figsize=(4, 4))\n",
    "plt.hist(test_prods[:], bins=20)\n",
    "\n",
    "hexbin_visualize(test_prods[:, 0], test_prods[:, 1], gridsize=40)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the coarse-grained variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of the variables $\\mathcal{H}$\n",
    "\n",
    "First compute the coarse-grained variables $\\mathcal{H}$ for each sample using the optimised map and exact discretisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "case_no = case_no\n",
    "vind = V_index\n",
    "\n",
    "#test_pr = net(Vs_dict[case_no]).numpy()\n",
    "\n",
    "test_pr = np.heaviside(dot_prod_V(Vs_dict[case_no][:, :, 0],\n",
    "                                  None, filters[-1]), 0)\n",
    "\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "\n",
    "\n",
    "if hidden_dim > 2:\n",
    "    ax = fig.add_subplot(projection='3d')\n",
    "    ax.scatter(test_pr[:, 0], test_pr[:, 1], test_pr[:, 2], s=0.01)\n",
    "    #ax.set_zlim(-1,1)\n",
    "    ax.view_init(10, 50)\n",
    "if hidden_dim == 2:\n",
    "    ax = fig.add_subplot()\n",
    "    ax.scatter(test_pr[:, 0], test_pr[:, 1], s=0.01)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse the statistics of bitstrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({(0, 1): 21653, (1, 0): 21493, (0, 0): 5920, (1, 1): 934})\n",
      "\n",
      "01\n",
      "10\n",
      "00\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "counts, labels, all_labels, hist = embedding_statistics(test_pr, CG_params) #, hidden_dim)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 3.5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 118.11x90.5512 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if hidden_dim == 4:\n",
    "    fig, ax = plt.subplots(1, figsize=(5.5*cm, 2*cm))\n",
    "elif hidden_dim == 2:\n",
    "    fig, ax = plt.subplots(1, figsize=(3*cm, 2.3*cm))\n",
    "\n",
    "if vertex_type == '8':\n",
    "    colour = 'b'\n",
    "elif vertex_type == '3':\n",
    "    colour = 'r'\n",
    "\n",
    "freqs = {key: counts[key]/len(test_pr) for key in counts}\n",
    "\n",
    "ymax = 1.1 * max(freqs.values())\n",
    "\n",
    "ax.axhline(1/8, color='gray', linestyle='-', label=r'$1/8}$', alpha=0.5)\n",
    "ax.bar(all_labels, freqs.values(), color=colour,)\n",
    "\n",
    "ax.set_ylabel(r'${\\rm frequency}$')\n",
    "ax.set_xlabel(r'$\\mathcal{H}$')\n",
    "ax.tick_params(axis='x', labelrotation=45)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.set_yticks([1/8])\n",
    "ax2.set_yticklabels([r'$\\frac{1}{8}$'])\n",
    "ax2.set_ylim([0, ymax])\n",
    "ax.set_ylim([0, ymax])\n",
    "ax.set_xlim([-.5, 2**hidden_dim - .5])\n",
    "\n",
    "#ax.legend()\n",
    "\n",
    "#plt.savefig(scale+'_'+vertex_type+'v_dist.pdf', dpi=500, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['01', '00', '10', '11']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the bitstrings around a cycle (if a unique cycle exists)\n",
    "\n",
    "sorted_labels = [labels[0]]\n",
    "temp = labels[1:]\n",
    "\n",
    "while len(temp) > 0:\n",
    "    \n",
    "    for check in temp:\n",
    "        if hamming2(check, sorted_labels[-1]) == 1:\n",
    "            sorted_labels.append(check)\n",
    "            temp.remove(check)\n",
    "            break\n",
    "\n",
    "sorted_labels\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Establish the clock variable according to the cycles found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_vmax=0.25\n",
    "edge_vmin=0\n",
    "width=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_dict = {389: [796, 797, 798, 799, 800, 801, 802, 803],\n",
    "              196: [797, 793, 7438],\n",
    "              274: [796, 46282, 792],\n",
    "              16704: [802, 33336, 39808]}\n",
    "\n",
    "# TODO: improve this"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8-vertex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1101': 0.125, '1100': 0.125, '0100': 0.125, '0110': 0.125, '0010': 0.125, '0011': 0.125, '1011': 0.125, '1001': 0.125}\n"
     ]
    }
   ],
   "source": [
    "#sorted_labels = ['1101', '1100', '0100', '0110', '0010', '0011', '1011', '1001']\n",
    "\n",
    "#freqs = {key: 1/8 for key in sorted_labels}\n",
    "\n",
    "#print(freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "region_of_interest = np.unique(edges_dict[389])\n",
    "\n",
    "weights = np.ones((len(region_of_interest), 1))\n",
    "\n",
    "R = small_V_visualize(region_of_interest, weights, edges=edges, nodes=nodes,\n",
    "                      nodepos=nodepos, node_size=50, figsize=(1, 1), cmap='Pastel1', title=\"\")\n",
    "\n",
    "\n",
    "weights = []\n",
    "label_dict = dict()\n",
    "t = 0\n",
    "for edge in R.edges:\n",
    "    label_dict[edge] = sorted_labels[t]\n",
    "    weights.append(freqs[sorted_labels[t]])\n",
    "    #print(freqs[sorted_labels[t]])\n",
    "    t += 1\n",
    "\n",
    "weights = np.array(weights).reshape((len(region_of_interest), 1))\n",
    "\n",
    "R = small_V_visualize(region_of_interest, weights, edges=edges, nodes=nodes,\n",
    "                      nodepos=nodepos, node_size=0, figsize=(3.8*cm, 3.8*cm), cmap='Blues', title=\"\",\n",
    "                      label_dict=label_dict, edge_vmax=edge_vmax, edge_vmin=edge_vmin, width=width, \n",
    "                      savename='8vertex_clock_sigma2.pdf', fontsize=6)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3-vertex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {},
   "outputs": [],
   "source": [
    "v3_id = 196\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "will now remove 00 with frequency 0.03854\n"
     ]
    }
   ],
   "source": [
    "freqs_cleaned = freqs.copy()\n",
    "sorted_labels_cleaned = sorted_labels.copy()\n",
    "\n",
    "spurious_code = min(freqs_cleaned, key=freqs_cleaned.get)\n",
    "\n",
    "print(\n",
    "    f'will now remove {spurious_code} with frequency {freqs_cleaned[spurious_code]}')\n",
    "\n",
    "del freqs_cleaned[spurious_code]\n",
    "sorted_labels_cleaned.remove(spurious_code)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01': 0.42298, '10': 0.42728, '11': 0.1112}\n",
      "['10', '11', '01']\n"
     ]
    }
   ],
   "source": [
    "print(freqs_cleaned)\n",
    "print(sorted_labels_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 966,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_of_interest = np.unique(edges_dict[v3_id])\n",
    "\n",
    "weights = np.ones((len(region_of_interest), 1))\n",
    "\n",
    "R = small_V_visualize(region_of_interest, weights, edges=edges, nodes=nodes,\n",
    "                      nodepos=nodepos, node_size=50, figsize=(1, 1), cmap='Pastel1', title=\"\",)\n",
    "\n",
    "label_dict = dict()\n",
    "weights = []\n",
    "t = 0\n",
    "for edge in R.edges:\n",
    "    if 389 in edge:\n",
    "        label_dict[edge] = sorted_labels_cleaned[1]\n",
    "        weights.append(freqs_cleaned[sorted_labels_cleaned[1]])\n",
    "    else:\n",
    "        label_dict[edge] = sorted_labels_cleaned[t]\n",
    "        weights.append(freqs[sorted_labels_cleaned[t]])\n",
    "        t += 2\n",
    "\n",
    "weights = np.array(weights).reshape((len(region_of_interest), 1))\n",
    "\n",
    "R = small_V_visualize(region_of_interest, weights, edges=edges, nodes=nodes,\n",
    "                      nodepos=nodepos, node_size=0, figsize=(1.3*cm, 1.3*cm), cmap='Reds', title=\"\",\n",
    "                      label_dict=label_dict, edge_vmax=edge_vmax, edge_vmin=edge_vmin, width=width+0,\n",
    "                      savename='3vertex_clock_sigma2.pdf', fontsize=2.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual information estimates with frozen CG maps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimate $I(\\mathcal{V}:\\mathcal{E})$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1\n",
    "\n",
    "L_V = Vs.shape[1]\n",
    "L_E = Es.shape[1]\n",
    "input_shapes = [(L_V, n), (L_E, n)]\n",
    "\n",
    "vbmi = VBMI(batch_size, input_shapes=input_shapes, layers=2, embed_dim=32, hidden_dim=64, activation='relu',\n",
    "            shuffle=100, learning_rate=5e-3, iterations=250, use_dropout=True, dropout_rate=0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f741349e6b884c7998b8d4c0c11cd5cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/6250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mis = vbmi.InfoNCE(Vs, Es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$I(\\\\mathcal{V}:\\\\mathcal{E})$')"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oIRpLgYGhGq9fhl8W8AeC//UFzgSE4PEz3585JsMXEPDLMlzeAOxuP+xuH5yeAGxuX9wgAwBKhQSLUYOKPD3K83SoMOtRnqdHWZ4OKkViC6QVCgm5ejVy9WpUDUzinywYcojGKV9AhssTgNMb/I1sSlFOQq9z+wLY09SHynw9qiyZ+wfPL8uDvhfQDIScPU19ib9HQI7YC4loNHwBGd0OL3pdwV6OXtfQ732wuX0pqxYsSUC+QYPCHA2KTToUm7QoM+tQnKtDWa7urApeTnYMOUTj1MeNPRE1VQpytDDr4w/7yLLAR4094b2CDrXZMxpyBt8wRtvTLQTw9wPsxaHE2fp9ON7lDO9yfrqvH+02DwIJJBiFBKiVCqiVCqgUwWEclVKK+F6tUISHeILPBSf0qgZeY9AoYdKpYdKpYNSokKNTodCogUrJsnWpwJBDNE4NLRrnH26PJADWfl844CTD6fHDoFEmPebe4/RCApBvTK7iL9FYCcgCLb39ONbpwLEuB460O9AdZ98prUqBfKMG+Xo18gwa5BvVyNdrkGdQB48bgnNPFFk69yTTzp1igVIhYffJvrh7lGUCQw7RJHaq1xUutAcEJ0GG5gd83NgDvyxDloFKix6zSnPjvk+ou7+xy4lzKs3QqBTYPTARODTfZnBY8vgDsPVz9dNEFZqDIhCs0aKUJASEgFqpQEAO/jdRQgi4vAG4vIGBpdHB2i8KSYrYSsMvywjIAj1OL9ptHjT1uNDU64J3yHwXCUCZWYfqAgMq8vSozA/+N9+gztrJs/EUmbTotHsy3QwYtMHeKQApqcVzNhhyiCYBf0CGHKM7/mCrHY1dTpSZdTjRdWZZqUoZeQM51dMfN+S0WvvxeYst/HjHse6I2hh7mvvg9ctwevwoNeswr8KM9w5H7s90pN2BIpMWRSbtqH9GSi2/LONktwuNXU409bhg7ffBF5Dh9Qe/AgPLnf0DBehikRBcymzQKKGQJBTmaKBWKpBnUEMIhIvX9fsCsHv8sPf70O8LRNV8SYZercSUQiOmFBkxvdiEukIj9OO4GGS+UY1eZ/AXhAVVeXhrf3uGW4SI3i0xqGqOXqPEgqq8DLToDIYconHGF5DRbnMnfP7pvn7sP22L+7zHJ0cEHCD2ZpPxHGiNfu/BNWp6Bw0PtFndmFdhjtnG0339GS3/TmcIIdDl8OJUrwuH2x041ulAU48L/rNJG0D49hcqxuhIYvhUrZRg1gd7W5QKCRDBXoPQ/VUxcDzfoEZRjhYV+XrUFhhRatZNmCGmhdV5UCsUcXevz5R4JSDOn1aY5pZEy4qQs23bNvzyl7/Erl270NraildffRXXXHNNQq/94IMP8MUvfhHz5s1DQ0NDSttJlA0+O9UX/k1uJB1297AB52x12j2Qk9w+afuQXbYHSya80djxBWSc6u1HY5cT+1ttaOpxoSfG3BWDRokZxSaU5+tQnKODWiVBq1JCMzDJ1qRTQatSQqdWQKkI1mgJ9c5IUrB6tUqhQK/Li4AQ6HZ44ZdlWPt9UEgSJAQn9urUSuTqVTBp1dBrlDDpVFAppHE3nDSWSs06FBg1sPUnP68ulaoLDKgrNIYf69TKYWtapVtWhByn04kFCxZg7dq1uPbaaxN+ndVqxZo1a3DppZeivT3zXXZE6ZBIwHH7AjjV64rqoTkbQoiIm0yH3R3eRDMZw22psPdU8u9Hyen3Bv9uNPf2o6nHhdN9/TF7aZQKCeXmYBXbmaXBYZ4ikzbpXhH1oJEhw8Ac9Bxd8NZTW2CM8YrUUCiAOWVm7GsJ/h0rydWNm1BdaNLG7AEFgMU1+Tjd1482a2Z+lhklpojHc8pycbjdnjX1eLIi5KxatQqrVq1K+nXf/va3cf3110OpVOK1114b+4YRZRFfQMbRDkfc5wWCy8Rb+vpxpMOedA/LSLYe7sTsslwoFRIKc7TcNiGLCRGscNtmc+N0nxttVjdO9brQ7fSiI85E1RytCrUFBkwpysHMEhNqCgzhfYwmArVSgVKzLhxy9BoFltZasLupNzyHSKEAFlblhyfN5+hU8Phl+IYp1jeWZpWZUGDUQiC4N9WBGL2wSmVkyLQYg1WHMxVyhtKplTinMi/TzQjLipAzGk8//TSOHTuGP/3pT/jpT3864vkejwcez5n/uW221HXhE6XC8U4nWnrj78LdkEQRvdHwB0S4p2VpnSVlhdEoMR5/AL0uH3qdXnQ7vegd2DCx0+HBqd7+Yee75BvUqLYYUGUxoGygt6bYpJ3Qw0GxehbMBjUumlkUrrWkkILVhRdW5+FElxNzynOhUSpwvMuJpu6x3e9pKKVCiljpaNCocKzDAa9fjpiQn6NVoa7ICK2KdXUSMS5DzpEjR/DjH/8Y7733HlSqxH6EjRs34oEHHkhxy4hSx+nNnrH40dTbodhCvS79vgBs/cFy/3aPH30uH2z9PlgHvvp9wWXUHn8AXr+c0IqjXJ0K1RYDCnK0mFJohMWoQWW+Przcd6IrNetQkaeHJCFcKLPUrEOb1Y2KvGCgiBXsQvtBhcwoMSUVcuZVnBkWS5RRG30vO29KAWxuHwqG1JqaOkx188U1+VApJXTaPWjsTGx7lMH0GiUWV+dDQOB4pzOqh+iimUVo6nHheKcT5Xn6pN8/3cZdyAkEArj++uvxwAMPYMaMGQm/bsOGDVi/fn34sc1mQ1VVVSqaSDRqoZogXr+MXpcXLm8Axbla7D9tS2rFUzoMXipKwwv9efa6gsNFrVY3Ou0etNnc6HV6h927aDhalSI4XGHQIH9g2KIwR4PSXB2qLIak6tHEU1toRPnAPknbDnee9fulU6x5LPMqzJhTlpuyrRIunlUMpUJKOuQsrs6LOqZRKSLC1rCvr8mHy+uHZSAQ5eqC+1TtbuqFw534LyXTinPCS+xLcnVRIUelVITnZ+XECGbZJvtbOITdbsfOnTuxZ88e3HHHHQAAWQ7uX6NSqfDmm2/ikksuiXqdVquFVssaHJTdDrc70NwT+Rtjtoy1D5bKFVvjlSwE+lw+NHY50Wrtx8keF7odXnTaPXD7AiNGQo1SEVxRpFPDpFXBrFfDbFDDrFMjz6CGTq2EUauCVqWARhXcFVqvTr4SdbJy9SoYNOPrVqFQINxTE/v52Ncs0Q0vh5pdnguHO1gHKtlieCW5OsyvjD2pOBmhuTmDaVQKnDelIG4tnYXVeVHD3CW5uvD3RSYtFlTloc/lxclBPVmSJI2b3sDx9TcXQG5uLvbu3Rtx7NFHH8Xbb7+NP//5z6irq8tQy4jO3tCAQ9nJ65dxpMOOE90uNHW70OnwoM3qhneYrTW0KgXMejWKTVqUmnUoNulQkqtFgVGLwpzs2LsoNJQzmFY1/iYff2F6UVLX85wqM452OOKuYBpObaERFTGGbZbWWvDJieyqZxNSW2hEZX5kmwtN2qhjAMJFOoPL+sdHsBksK0KOw+HA0aNHw48bGxvR0NAAi8WC6upqbNiwAS0tLXj22WehUCgwb968iNcXFxdDp9NFHSciOltCCLRa3cF5CAMbOx7vdMYsjCdJGNgqQI9qiwFFOVqU5OqQo1XBpFNl9cRehSI4lDM05Ay36Wu2SjYwBnf+1o184hClZh1qC2L3GJkN2XPdJClyQ1y9RgmdWgkhBPIGtrNYOEJl4kxu5ns2siLk7Ny5ExdffHH4cWjuzE033YRnnnkGra2taGpqylTziGgS8csymnpcONLuwOF2O451OmOuVMo3qDG92IQqix7leXqUmHQoMmmzbu+ewcryghNxd57ojThu0qnC23bkGzURVaqzQU2BIWK4ZKjBN3F1ilYd1RYaoupOjabnBwDmVuTiRJcLTo8f5XnJh6tklZp1aO2LHvaWJAlLai0p//xMyoqQc9FFF0EMsx71mWeeGfb1999/P+6///6xbRQRTQp2tw+nevtxpMOBox3BLQyGTgRWKyXUFhhRZTGg2mLAtKIclORm35LrL8wogsvrjwoxg+UZIudtqFUKnDulIPy4viYfnXYPPm3uS1Uzk5KjU2F6iSluyDFolFg+tQAfN/bA7vajzJya0DCt2ITKfAPePxK/YneiyszBUOzxy2nZR2tWaS7yDRocHViSPnS11kSWFSGHaLJzevzYeTL+jYnGhsPjx8luJxq7nDjRFdylerjtC6aX5GB6cQ4q8w3QJNhDcO4UCz46PvxcDEuOBj2Ose8t0agU0Kg0yDOoYxZrLDdHzrmoshhQWxg9DFFg1MBsUI+4esakU6GmwBi1kqi20IgTXckvXx6N86YUQJIkLK7JR4/Ti6IEVyONxuDiiJacxINCvlGNwhwtuhyecC0chUJK20ahSoWE8jw9SnN14d3gJwuGHKIscLDNlraqqpOJX5bR2OXEZ6esONBqw4k4vQFFJi1qCwyYXmzCjJIclOfp425fYNAqUV+TH7WTeohJp46aA5Eoo1YVVYMoV6+GrX/k6tKlg3owZpaasPPEmUq+RSYtZpSYwjfVPIMaTm8A04pzYg6vKRQSlsYYxlCrFBF/T1XK4AqvoaYV56DH6U2o3fFUWQxo7nFhWnH8mjChtgLBisaDVwalysxSE9ptbsxPYqhqbrkZOrUSNWncxiIWhUKCAtnV+5hqDDlEGSSEwN4Wa8IbbtLw/AEZbTY3jnU6caDVhs9OWaNWPBWbtKgtMKK20IAaixHVFkNSv1FLkEZccbR8agG2H+2O+7zFoMHUwpyI1TfLpliQq1Oj3xvABwObmC6fWgCjVhV3CfBgg8OASRes5OsLCHQ6PCgxaSMm4y6ptUCWRdK1YhZX5+FIhyPcC1WYo4m4dnVFxjEZCinP02NmqQlTioxxex3yDOqIYJcuVQOVohOxfGoB/AExobbHGG8Ycogy5GCbDad64m/TQMOThUBrnxsne4LbXTT39secT6NVKbCgMg+zykxYUJk34mqhQpMWXXH2dwKCk1zjCdU7iVVX5rI5Jej3BtDj8qIsVweFQsKFMwrRafegNFcXDiF6jRJLavMhi9hVcGM5p9IcdSOVJAkalRRzeTMQv1bMcEw6NRZX58PtC6DP5QvPS1oxrQAKSYpow3DzLIcyalWYWWqCaWCvKONAcBoccIb2aC2uzk9ZQb+xkuifH6UO/wSIEuTy+mF3+8esS5wBJ3luXwC7mnpxsNWOPc29cPuih/j0aiUq8/WYU5aLOeW5mFJoTGqC8MKqvGF7Tob788+Ls2zYoA3etPUaJSo0Z0KHVqWM2K/ozPtE9obU1+Tj01N9UVWvVUoJM0pMKE7DMM1gOrUSpeYzgSZWqBs8h6m6wBCxLYJeo0T/oN3oJQnhQnbxem6W1ubDLws43H5I0uhCGk0+DDlECQoNP7SZ3LAYNZCFgEmnjqoyGo/L68fpvn5UWQzjssBaprh9AexrsWL7sW7sb7VF1KfRqhTBjSbzDajM16O2wIhKS/z5NCMZvBFiLAuq8lA4ZMKpUiEhIAvkG9Vx/1xXTC0cVXtC8o0aTC3KwaE2O4BgSKjKN6A6To2WbDC7LBf7W22othhQOFAv6FinA1OLcmDWqxMaghtMkiSolRLyJ9HKIDp7DDlESeq0e9A5aDjjsjklCb0uFJKs/X7U1+SnpG0ThbXfh70tVuw9ZcW+09aIIaiSXC0WVuVhYWUephTFnjg71NyKXHzeEn8riktnF6PP5QtXdJ1dnos2a394rtTcilyY9eqIHguFApBloL42H7kxStzPKc/FoXY7Fo1QZG00zp92dqEpHXTq4EaPIWa9OuLxYLPLctPVLJpkGHKIRtDt8KBxmOWwbx9sH7GM/OBicr1OL94+mNxvsRNZn8uLkz0unOrtx+m+fjT3uHB6SNXdijw95pbn4oJphSgz6xIeflIozpT4Hy7kSFJkD0FFnh4WgyY8AbjYFL0n0YXTi+Dxy3GXWZfn6ZNq60gKBnqQUlXsLlNCAZIoFRhyiEawZ8gGdkPJMvDuoU5UWQyYWWqKer65xxUeZhj8mslGCIFelw8nu5042RPc8+lkjwvWOMuMawoM+OrCClw8swiV+Xp82hxZiyWRejTAmRL/s8tzcbLLiYIcbUJ7hA3eZT1WTFErFSPWGxnLYoEGjQoXTC+ccDVOOHRLqcSQQzRGQjU9Qr/xB2SBXSd7z6pWyHglhECPM9hDc7LbFQ42dnf09giSBJSZdaixGFGcG1zeXW0xwKxXh4cChRAoyNFAqZBg7feh2mKASafG0joLDrXZw9dYp1ZifoU5vDRbGhRPKvL0qMjTwx+QYXf7YhbLG2xwmMiWwsYTaSnyOZVmODz+hOe0EY0GQw5NWkKIMS/L/87BDuQZ1FhSa0GbzT3hA44sC9jcPnQ7vehxetEcCjU9rpj7PSmk4DBOtcWA2gIjagqCE4ZH+m1ekiQsijGfw6xXY1mdBW8fbIcsAxX5epgN6vD+S+Uxlk+rlAosqbXgaIdj2Kq8aqUCS2stkBRj2yNDQcW5OhRnuhE04THk0KTU4/SiobkXM0pMMZfwhiRT6yMk1EMgx9ilerzy+mWc6A7Wozk9MCG3zeZGp8MTrqo7lFKSUJ6nQ02BETUWw0CgSXx7hGQmZ6+YWogepxelA0upF1Sa0ePyotAYf7VUXaERaqWEwmG2AcimnaSJKHkMOZT1uhweqBRSVO2Qs7G3xQpZBg622uOGnD6XF7ubRr+fVL8vMPJJWSBUPn8wp8ePz05ZcbjdjhPdTpzucyMQJ/BJEpCnDy6lLzfrUVNgQE2BEZX5+pjzR/KN6mErPNcUGDC9JHpu03B0amVEr41KqUCxafjaMUqFlPEy+0SUWgw5lNXcvgAaBib+Fpq0KM3VjbqUe7LDU/tabKOeIHyk3R5R/CxbWXI0KDIFJ+I2dbvw8Yke7G+1obnHhaGRxqxXo7bAgDKzHgU5GhQP/HnkGzRJFWabU2YOr1oa3I7QVgElGSjVT0QTE0MOZTXPoIq2XXYPuuyeuCHndF8/mnpcWFCZB71GCX9AhsPjh1mvht3jx8cDK3FydKoRN8PstHvgPouemJPjIOAAwQq9W/a34cn3G3G43RHxXIlJi8U1+agrNKK2wIh8g3rUc1M0KgW8w1zzGSUmOMx+9PsCMWvOEBGNBkMOZTUR1Z8AHG63Y1pRTlTvwf7TwTooh9rtWFiVhz3NfbDGWEHjiLHCZ6hPm/tG1+BxoqW3H9uOdGLnyd7wEm6lJGFRdR4WVuVhdtnY1i5ZUJmHT0/1QRYCWpUC04pzcLqvHzWFRigkIEeriltvhohotPivCo07Td3BpcgapQIzSnOiVuaEJsLGCjiTmSwLfNjYjfeOdOFIx5lem3KzDmvPr0OVRQ+V4uxrsJw7xQKdWolTvf04NvA5ZoMaFwxU6VUoJNQWGlFbyPkwRJRaDDmUER02N5zeAOpGuNHFW9zU6wzO35AkYF6FOeq5D493j6pdsizQMcwO1OORy+vHh8d78FFjN451BpdMSxKwck4JvrmsGhdOL4JSIcHh8WP/aVvEsvcZJSYcbrfHe+uYTAPDTfohNV24oSIRpRtDDmXEZ6eC1WstBs1ZLdONVy03kSGpkNCmgQBwoM2G1j73CK/IPotr8rH75JmVYH5Zxuenbdh+rBufNveFN7XUq5W4Ym4JrllUgYtmRlYpydGqsKzOgn0tVnTY3fjijGIoFRJMOhV2nUxsldngXbiLTVoU5GiQP4ar4oiIksGQQxnlCQQAxA45ocJywwlNDj7d1z/qNjR2OlFg1CDPoBmXAQcALEYNqgsM+LixB+8d6cT2Y90R1YWnF+fghnOrcensYtjcfkwvjr9EO9gzdqZ3LN+oQY5ONWJwnF6Sg2rLmeX4CkXsAn5EROnCkEMZJcXcFSho6H5PsQgBvLX/7De7PNrhGLc7g/sCMv7S0ILnP27Ch4P2cjLpVDivrgB3XT4dc8vNw7zDyJbVWrC/1Ya2QRtnziozQa9WQq9RRuzOTUSULfgvE2WVVGy1kIg+lw+eEZaVZ5MFVXk42mHHX/e24b/3tqJnYI6SQgK+OKMIc8vNmF9hhlIhnXXAAYK9MvMqgnsNhXp0hqsUTUSUDRhyKO0G72l0rNMBvUaJHK0Knzb3wenx47wpBZBHsZ3C2RrtZOV0W1prwV/3teI3bx1Guy04SbokV4tvLK3G6qVVKM/T49PmPnSmYAL1OZVmHGyzo5aVgoloHGDIoZQSQqDV6ka+QQO9JrjaZnAlYIfbjw+PdeOyOSXhm3KPy4vugeq36eQPZMdeU6VmXcSw0PKpBdhxrBsOjx9b9rfj/7y2D2224PNmvRor55Tg/14zL2KHarNenZKQY9CosJjzbIhonGDIoZQ61dsfnltz2ZwS2Ny+mJOEnYN6d0LbOExWpebg1hUNTX0oztWiscuJP2w7jt1NveFVUsUmLdaeX4tF1cGKxLohy7Ur8vVotbpRnBt/80kioomOIYdSKjRXJPT97jhLkQcPYU12aqUCRo0Sfllg4+aD2DFoGG12qQm3XzINl88piSqCOPQ9lk8tSEdziYiyFkMOnTUhBGxuP3J1qqhJw4MHgOIFHADYO1A3Z7Lz+mX892en8cwHJ8JVidVKCSvnlOI7F03F3PLcjEzMJiIajxhy6KwdbLOjpbcf1QUGzCiJrL8iMjCBeLyxu3041duPnSd78cmJHri8wdo/+QY1rl1cibUX1KEiT5/hVhIRjT8MOXTWWnqDc2yaul2oKzRCrTyz/xEjTiRfQMbhdjuOdTpxosuJNpsbnQ5PxPYVlfl6rFleg9VLq8d0k0wiosmGIYfOij8QWVtm66FOLK21hLdqYEdOcO+oT5ut2HWyF/tbbfAGouvxFBg1mFFiwncvmorzpxVynyciojHAkEOj1ufyYueJ6Hk2TT0uzDcEC9D1OtO/FDzTvH4ZRzrsONBqx8E2G072uCLCXp5ejdlluaiy6FFtMaAoR4uCnOAqqAtnFGWo1UREEw9DDo3a8S5nzOPtNjekFmBueW6aW5Q+uXo18g3q8N5aDo8fn53qQ0NzH/adtsE7pHpymVmH+up81NfmozJPH3Py8NTinLS0nYhossiKkLNt2zb88pe/xK5du9Da2opXX30V11xzTdzzX3nlFWzatAkNDQ3weDyYO3cu7r//flxxxRXpazQNOxTVZnVHFLSbSJbWWWDWq+EPyNjbYsUfth3H4XY75EHXI98Q7K2ZXZqLWWWmhHbi1qoUI55DRESJy4qQ43Q6sWDBAqxduxbXXnvtiOdv27YNl19+OX72s58hLy8PTz/9NK6++mp89NFHWLRoURpaPHmFVkt12j2w9k++oahSsw4A8PjWY3h2x0m0DCpsWJmvx6KqPCyqykeVJXZvDRERpY8ksmyNryRJI/bkxDJ37lysXr0a9957b0Ln22w2mM1mWK1W5OZO3GGVsSSEwEeNPeENGicbu9uH94914W972+AcWOZtMWpw/bJqVOTpUWQ6u+rC51SZUWzSjUVTiYgmrGTu31nRk3O2ZFmG3W6HxWKJe47H44HHc2YvH5vNlo6mTSi+gJiUAaep24V3D3fgw8ae8FybmSUm3HJBLb66sAI6tRJ7mnpH3G9LrVLAF2en84p8PYpyuAUDEdFYmhAh59e//jWcTieuu+66uOds3LgRDzzwQBpbNX55/TJUCilqGfN4Hn2pLTSiqccJOXbGiOL2BfBRYw+2HekMTy4GgHkVuVh/+QxcPLM4YjhqXoUZWw91xn2/KUVG1BQY0e304LPm6OrOs8vYm0hENNbGfch5/vnncf/99+Mvf/kLiouL4563YcMGrF+/PvzYZrOhqqoqHU0cV9y+AN4/0gUA0KmVqLYYIEnAoTY7DNr4eyVlu2nFOdCqFOHNQgfzBWS0Wd043dePFms/Tve6caDNBs9Ar4tKIeGLM4rwv78wBcvqLDHn2gwugDhUdYEBNQVGKBXSwHBUZMipshjO7ocjIqKYxnXIefHFF3HrrbfipZdewmWXXTbsuVqtFlothwNGsrvpTN0bty+Aw+1nQoHLE8hEk85KbaEBhTGGgXqcXuw82YPPTllxpN2BQIypaSW5Wnx1YQXWnl+LMvPI2yooFRICcuT7VFr0UVtdWHI06BkY2lpSm49cHasaExGlwrgNOc8//zxuueUWPP/887jyyisz3ZwJYzwGmeFUW4zQDCzN9gVkfHKiB28f7Ahvfhli0ChRkadHeZ4eZWYdphQaUVdoRE2hMaGAAwDLpxaEe8EqLXqoFBJqC4xR5w3uB8pLYGk5ERGNTlaEHIfDgaNHj4YfNzY2oqGhARaLBdXV1diwYQNaWlrw7LPPAggGnDVr1uDhhx/Geeedh7a2NgCAXq+H2WzOyM8wETg8E2NS8YKqPHza3Bd+7PYF8J87m7Hp3WNoHajdI0nAkpp8TC3KwfwKM4pN2qhhKI1KgZqCxIeSdGolvjCjCHa3DxajJu4S8iqLAd0OLyw5DDhERKmUFUvI3333XVx88cVRx2+66SY888wzuPnmm3HixAm8++67AICLLroIW7dujXt+IriEPJIsC7x9sCPTzYhr8FCQQhHsoTkRp+LypbOL8XFjD1xeP451OvHU+404PRBucnUqXDi9CPdePQcluTo0dbsihuSGvk+qat24fQFoVQrW0iEiStK4W0J+0UUXYbisNTS4hMIOjZ09zdF7UGWTqoEJ0D1OLxZX50OpkOKGnF6XD+8d6cK/7zgB+8CS99JcHb5z0RScU5mHklwdSnKD9Wh06vgThlMZQHTq8TuJm4hovMiKkEPpJ4SIuIn3On0ZbM3IphYZIUkSpsbYv7LIpEV5nh5t1n78585T+N7ze9DvC4Rft/b8Ovyv+sqYwSJeAb/yvMTm4RARUfZiyJmE7G4fdp3sxdSinHGxfHlBVd6wvSpufwC/fvMQXt59Cr5AsEdwfoUZt188FSvnlEbV+xks1vuG9qYiIqLxjSFnEhFC4EiHA00Dxe0OtdlRkKOBMwtXVFlyNFhcnQ9ZFnFDSqu1H//1aSt2nuwJb455bp0Ft188DRdOLxz1cBOnyRARTQwMOZNIp8MTDjgh2492Z6g18eUZ1FhcnQ8AMQOOtd+Hh986gn/ffiJc3+aSWcX47kVTsaQ2/tYeREQ0uTDkTCIeX4J7GmTQcCuaArLAi58041dvHkKPM1hM7+KZRfjBFTMxt3z0pQNMOlV4gjIREU0cDDmTxKE2O5p7XCOfmGaDl4bXFhrjBpyPG3tw/+ufY39rcGPVacU5uPeqOfjCjBgzkZO0tNYCb0AOF/LjaBUR0cTAkDMJCCGyMuAAkVWCLcbo4ngtff342eYD+J/PWgEEe13uvmwGblxeM+x+UclQKCToFEpUFxjg8ckwcZsFIqIJgSFnAvMHZPhlAc0YhYGzsbTOAqfHj/2ngz0xBo0S500pgEIh4YLphXD7AhFbHPgCMja9ewyPvnsUbp8MhQR8Y1k1/unyGSiIsRfVWBi6xxQREY1vDDkT2HtHuhCQBZZPLch0U2DWq2HWq8MhBzgzqVinVkbUsPn7gXb8bPMBHOsMFvtbVmfBfVfPOat5N0RENPkw5Eww/d4A9rdaUVNgDM912XEs9SuoCnI06B7YWXu0Wq39+L//vR+b9wb3IrMYNbjv6jn4yoJybn9ARERJY8iZYPa3WtHr9KHX2ZfWz51fYYbLF8C+U1a4vMnV3fEHZDz/STN+8deDsHv8UCok3HZBHW6/ZBpyOT+GiIhGiSFngsnEMvFLZhVDoZCQq1RgxbRCvLW/PeL5WDt5hyb3NjT34Z5X9+LzgWGsRdV5+Jdr5mNOOTdNJSKis8OQQ2dlTnluVMG++ZVmHGl34Jyq4Bwak/bMX7Nzp1jQanWjKEeLe/+yD//vw5MQIrg7+PrLZ+DG5bVQDrMNAxERUaIYciaY+Hu5jy2lQkJlvh5lZl3Uc4N3+R7KpFOjz+XDN5/4EAfb7ACAry2qwE+unI3CFK2aIiKiyYkhZ4KQZYH9rTb0JzkfZrSW1Vlg1Cb/1+cvDS2459V9cHj8KMzR4uFvLMT50wpT0EIiIprsGHImiDabG21Wd1o+a9mU5ANOvzeA+1//HC/ubAYA1Nfk45FvLkJFnj4VTSQiImLImQjcvkBE/ZlUS3bF074WK77/wh4c63RCkoA7L5mO710yDaosKFJIREQTF0POOCeECG+LkA75MbZeiEcIgRc+acZ9r38Or19GsUmL335jIVZM5fAUERGlHkPOOBcq+JcOF0wvhFaVWO9Lq7Uf//znz/DeQAC7bHYxfvm/FiQVkoiIiM4GQ844l6qIU11gQIFRgz1NfeFjg7deGM7mva348cufweb2Q6tSYP3lM/CtC6dELTUnIiJKJYYciim0WaVBo0y4grHbF8BP/2c//vRhEwBgQaUZD61eiKlFOSlrJxERUTwMOeOUPyBn1cTdDpsb6/60C7sHen6+/cUp+MHKmVBnURuJiGhyYcgZZ5weP1qtbpzocsKgUcLjH/ttHPSaM8NS+UYNXN7+Yc/ff9qGm5/+GB12D3J1Kvzb9YvxxRlFY94uIiKiZDDkjCONXU4c63CEHye7EWYiSs06TCkyhh/XFBjQ0tuP0hiVjYHgDuf/+9mdsHv8mF6cg8dvrMcUDk8REVEWYMgZRwYHnFTQqhWYV2GOOGbQqHDxrOKY+0n9dW8rvv9CA7wBGcvqLHhizRKY9dw1nIiIsgNDzjggywKH2u1j+p6WHA16HN7w45oCAyrzo3cLBxAVcGRZ4Ld/P4J/e/sIhACumFuCh7+xKOHVV0REROnAkDMOtNrcaOkdfl5MshZX56Olrx8HBiolTx9YTTUSX0DGj1/ei5d3nwIArFleg/uunsudw4mIKOsw5IwD3jGaXDytOAcFOZrwiqeCJAvzWV0+rPvTLuw43g2lQsLGf5iP65ZWjUnbiIiIxhpDzjggxNiU/KstNEY81qmVuGB6YUK9MJ829+HOF/bgZLcLRo0Sj3xzES6dXTIm7SIiIkoFhpwsZ+334XinM2Xvn8g8mhc+bsK9f/kc3oCMcrMOT968FLPLclPWJiIiorHAkJPFArLAJ409Gft8IQR+/eZh/O6dowCAy+eU4FdfX8AVVERENC5kRTnabdu24eqrr0Z5eTkkScJrr7024mu2bt2K+vp66HQ6TJkyBY899ljqG5pmvsDYzMXRa5RYVJ2X9Gf/6OXPwgFn/eUz8Pg/1jPgEBHRuJEVIcfpdGLBggX43e9+l9D5jY2N+PKXv4wLL7wQe/bswU9+8hPceeedePnll1Pc0vT6fGDl09k6f1ohCnK0CZ8fkAV+9OfP8J87T0EhAQ9+dS7uvHQ6N9gkIqJxJSuGq1atWoVVq1YlfP5jjz2G6upq/Pa3vwUAzJ49Gzt37sSvfvUrXHvttSlqZfr1Or0jnxTHOZVmtFrdmFOe3NwZr1/G7f+xG1v2t0OpkPDYP9bj8jmcYExERONPVvTkJGvHjh1YuXJlxLErrrgCO3fuhM/ny1CrxobL60djlxP+sxiqOqfKjOJcHRZU5SW1QaYQAj/9n/3Ysr8dGpUCv/vmIgYcIiIat7KiJydZbW1tKCmJvPmWlJTA7/ejq6sLZWVlUa/xeDzweDzhxzbb2AwFjbWPjvcgIAu4vP60fq4QAhv/ehDP7jgJAPj99YsZcIiIaFwblz05ACBJkfNDQrVkhh4P2bhxI8xmc/irqio7i9gF5ODPYXWNvkdKQnJzZ4QQ+MUbh/CHbccBAA98ZS4DDhERjXvjMuSUlpaira0t4lhHRwdUKhUKCgpivmbDhg2wWq3hr+bm5nQ0NSP0muT2kPrNlsPY9O4xAMFJxjetqE1Bq4iIiNJrXA5XLV++HP/1X/8VcezNN9/EkiVLoFbHXuKs1Wqh1Sa+wijTXN5AUufXFBhQbNLB4w8gR5vYH6sQAr/ZchiPvB1cJv7/XTUHa5bXJttUIiKirJQVPTkOhwMNDQ1oaGgAEFwi3tDQgKamJgDBXpg1a9aEz1+3bh1OnjyJ9evX48CBA3jqqafw5JNP4gc/+EEmmj9mZHl02zcYNEpMLzHBbFCjOFeX8Ot++cahcMD5yZdn4dYL6kb1+URERNkoK3pydu7ciYsvvjj8eP369QCAm266Cc888wxaW1vDgQcA6urqsHnzZtx99934/e9/j/LycjzyyCPjevl4l8ODhqa+pF+XZ1CjviY/6de9tqcFjw4aomIPDhERTTSSGKvdH8cZm80Gs9kMq9WK3NzM78O09XAnfKPYbbzIpMWCqrykXrP9aBduevpj+AIC6744FT9eNSvpzyUiIsqEZO7fWTFcRaPfabwsL/HhKQA42e3Ed57bDV9A4Mr5ZfjhFTNH9blERETZjiFnnEtkF/EQh8ePbz27E9Z+HxZW5eHX1y2Akls1EBHRBMWQM84ohvyJ6RMMOQFZ4O4XG3C43YFikxaP31ifVEAiIiIabxhyskSig1UVeYbw9/U1+Qlv2/CzzQfC2zX8Yc0SlCSxCouIiGg8YsjJAtZ+HwKBxGLO4ILO+UZNQq/5865TePL9RgDAr76+AAuTnKhMREQ0HmXFEvLJrM/lxc4TvSl7/10ne/CTV/YCAO68dDq+sqA8ZZ9FRESUTdiTk2HJBpxkpgm39PXjfz+7C96AjJVzSnDXpdOTaxwREdE4xpAzzhgT3LKh3xvAt//fTnQ7vZhdlovfrF4IBVdSERHRJMLhqgxKZqfxuiJjwpOFhRDY8Mpn2Ndig8WowRNr6hMOR0RERBMF73wZ4g/I+ORET0Lnzq80o9ikhTQw63hOeS60qvidcE+8dxyvNZyGUiHh99cvRmW+Ie65REREExVDTgb0Or3osHsSPn9oD055nj7uuQ3Nffj5Xw8CAO69ag6WTy0YXSOJiIjGOYacDNh1MvHJxsnUs3H7AvjBS59CFsBXFpRjzfKa0TSPiIhoQuDE4yymUysxq8yU8Pm/fesIjnY4UJijxQNfmRse3iIiIpqMGHKy2KLqvIQrGu9p6sUfth0DAPzsH+YlXCiQiIhoomLIyWIGTWJ7Sw0eprpmYTlWzi1NccuIiIiyH0NOlrp0dnHCw02/eeswjnU6UWTS4v6vzE1xy4iIiMYHhpwslWjA2d3Uiye2HQcA/Owf5iPPwGEqIiIigCFnXHP7AvjhwDDVPyyqwOVzSjLdJCIioqzBkJOFlk2xJHTeb7acGaa67+o5KW4VERHR+MKQk2ayLIZ9fmmtBbk69Yjvs7upF0+8Fxym2shhKiIioihnVQzQ5/Ohra0NLpcLRUVFsFgS64GYzI53OYd93mwYOeAMXk31tUUVuIzDVERERFGS7slxOBx4/PHHcdFFF8FsNqO2thZz5sxBUVERampq8K1vfQuffPJJKto6IbRa++M+t6g6L6H3+MO24zgeHqbiaioiIqJYkgo5v/nNb1BbW4snnngCl1xyCV555RU0NDTg0KFD2LFjB+677z74/X5cfvnl+NKXvoQjR46kqt3jktPjh8cnx31elUDhv1O9Lvzu7aMAgP9z5eyEen6IiIgmo6SGq7Zv34533nkH8+fPj/n8smXLcMstt+Cxxx7Dk08+ia1bt2L69Olj0tCJYMex7mGfz9WN/Mfxu7ePwhuQsWJqAb6yoHysmkZERDThJBVyXnrpJQDA4cOHUV5ejpycnJjnabVafPe73z371k0SOToVFlbljVgb52S3Ey/tOgUA+KeVM7k3FRER0TCSnpPz+uuvY9asWViwYEHUc4FAAP398eecUGy5OjV06pG3cHj470cQkAUumlmE+pr8NLSMiIho/Eo65Pz3f/833nnnHWzcuBEAsH///vBzkiTh0UcfHbvWUdjRDgde29MCAFh/+YwMt4aIiCj7JR1ycnJyUFNTg+uuuw4A8KMf/QhtbW3BN1Mo8IUvfAEvvvji2LaS8PDfj0AWwGWzS3BOZV6mm0NERJT1kg453/jGN/CrX/0q/Pjw4cP4+9//Hn68dOlS/OUvfxmb1k0SGtXwfwyH2uz4789OA2AvDhERUaKSDjnLli2DUqnEtddei69//es4evQo/vSnP8Hj8YTPaWpqGtNGTmQluTrUFhiGPefhvx+GEMCX55diTnlumlpGREQ0vo1qW4eHH34YV111FTQaDZ5++mmsXbsW1157LVpbW/Hpp59GzNOhIH8gdn2c+ZXmYevjtPT142/7gsOB37+UvThERESJGvW2DmvXrsXatWvDj//+97+joqICkiThqquuGpPGTSQne1xRxxJZUfXchychC2DF1ALMLDWlomlEREQT0pht0Pn444/jjTfewCOPPIKnn3466dc/+uijqKurg06nQ319Pd57771hz3/uueewYMECGAwGlJWVYe3atejuHr7YXiY1dkbvWbVwhG0c3L4AXvikGQCwZnltClpFREQ0cY3pLuSXX345br/99qQ36nzxxRdx11134Z577sGePXtw4YUXYtWqVXHn9rz//vtYs2YNbr31Vnz++ed46aWX8Mknn+C2224bix8jbXK0w3ek/c9nrehxelFu1uGy2cVpahUREdHEMKYhZ7Qeeugh3Hrrrbjtttswe/Zs/Pa3v0VVVRU2bdoU8/wPP/wQtbW1uPPOO1FXV4cLLrgA3/72t7Fz5840t3z0ikzaEc959sOTAIAbzqtJaF8rIiIiOiPjd06v14tdu3Zh5cqVEcdXrlyJ7du3x3zNihUrcOrUKWzevBlCCLS3t+PPf/4zrrzyyrif4/F4YLPZIr4yyThCL86uk734tLkPGqUCq5dWpalVREREE0fGQ05XVxcCgQBKSkoijpeUlISLDA61YsUKPPfcc1i9ejU0Gg1KS0uRl5eHf/u3f4v7ORs3boTZbA5/VVVlNjjUjLBs/Mn3jwMArllUjsKckXt9iIiIKNJZhRyFQoG5c+dGHJs9ezaUypFXDQ01dLNJIUTcDSj379+PO++8E/feey927dqFv/3tb2hsbMS6devivv+GDRtgtVrDX83NzUm3cSypE1w2fssFdelqEhER0YQy6iXkAPDUU08hLy8v4tjGjRthtVoTfo/CwkIolcqoXpuOjo6o3p3Bn3H++efjhz/8IQDgnHPOgdFoxIUXXoif/vSnKCsri3qNVquFVjs+ekRe+LgJsgCWTynArFIW/yMiIhqNswo5N998c9Sxa665Jqn30Gg0qK+vx5YtW/AP//AP4eNbtmzBV7/61ZivcblcUKkimx7qPRJCJPX52cYfkPGfO4O9TDecV53h1hAREY1fGZ+TAwDr16/HH//4Rzz11FM4cOAA7r77bjQ1NYWHnzZs2IA1a9aEz7/66qvxyiuvYNOmTTh+/Dg++OAD3HnnnVi2bBnKy8sz9WMMa/BqKq06/mV//2gX2m0e5BvUuHxO7J4sIiIiGtlZ9eSMldWrV6O7uxsPPvggWltbMW/ePGzevBk1NTUAgNbW1oiaOTfffDPsdjt+97vf4Z/+6Z+Ql5eHSy65BP/6r/+aqR9hRIM34ayvyY973iu7WwAAX11YAa0q+blNREREFCSJUYzv9Pf345VXXkEgEMCll16KioqKVLQtpWw2G8xmM6xWK3JzUz/v5XC7HU3dwa0dLplVDIUielK13e3Dkp++BY9fxut3nI9zKvNS3i4iIqLxJJn796iGq6688koYjUbU19fjueeewz333IP+/v5RNXayCAWc8jx9zIADAH/d2waPX8a04hzMrzCns3lEREQTTtIhx+Px4N1330VnZyfmzp2Lf/7nf8a6devwne98B+3t7alo47gXkM90lvX7/HHPe2XPKQDA1xZXxF0+T0RERIlJOuRotVr867/+K4qLz+ylVFVVhd/97ne477770NLSMqYNHO+EENh6uGPE8zrtHnzU2AMA+MqC7Jw8TURENJ6Marjqhz/8YdTy7pycHPz617/Ghg0b0NfXNxZtmxA8fhmyfOaxyxuIed4bn7dBCGBBVR4q84evhkxEREQjG/Xqqr179+Kll17Cvn370NHRAa/XC4PBAFmW8dWvfhVbtmyBRqMZy7ZOCB6fHPP4X/e1AgBWzStNZ3OIiIgmrFH15GzevDk86djn82HKlCmYNWsWCgoKoFKp0NXVhQ0bNox1W8eloT03lpzo4Nfr9OLD48GhKoYcIiKisTGqnpxf//rXeOutt/CFL3xhrNsz4fS5vBGPZ8fYpuGDY10IyAIzS0yoKTCmq2lEREQT2qh6cs4//3wGnATJQ6oQ6TXRBf62H+sGAJw/rTAdTSIiIpoURhVyrrvuurFuxwQ2cq3FHQMhZ8XUglQ3hoiIaNJIKuSEtlaYN29eQudzOXl0T85Qp/v60djlhEIClk2xpKdRREREk0BSIWfp0qX41re+hY8//jjuOVarFU888QTmzZuHV1555awbON6NtGlGqBfnnMo85OrUaWgRERHR5JDUxOMDBw7gZz/7Gb70pS9BrVZjyZIlKC8vh06nQ29vL/bv34/PP/8cS5YswS9/+UusWrUqVe0eN3L1w1/i7RyqIiIiSomkenIsFgt+9atf4fTp09i0aRNmzJiBrq4uHDlyBABwww03YNeuXfjggw8YcBIghMCOY10AgBVTOemYiIhoLI1qCblOp8PXvvY1fO1rXxvr9kw4w83JOdntwmmrGxqlAvU1+elrFBER0SSQdMi57bbbsHjxYtTX12PBggXQ6XSpaNeEIQ+Tcj4Y6MVZVJ0Xc2k5ERERjV7SIefw4cN46aWXYLfboVKpMGvWLCxevDgcfBYuXAijkQXtQoabeHxm6TiHqoiIiMZa0iFn27ZtAIAjR45g165d2L17N3bt2oXXX38dfX19UCgUmDFjBvbv3z/mjR2P5GFSTkNzHwBgaS2HqoiIiMbaqDfonD59OqZPn45vfOMb4WONjY3YuXMn9uzZMyaNmwjihZxuhwenevshScD8SnOaW0VERDTxjTrkxFJXV4e6ujp8/etfH8u3HdfiTcn57JQVADC1KAcm1schIiIac6Pa1oESd6rXFfN4aKjqHPbiEBERpQRDTor5A7G7co502AEAc8qidyUnIiKis8eQkyHHOpwAgGnFORluCRER0cTEkJMBAVmgsSsYcqYWMeQQERGlAkNOGuXogvO8T/W64A3I0KoUqMjTZ7hVREREExNDTooplVL4+9B3xzodAIApRTlQKKQYryIiIqKzxZCTYoEYE4+PdgRDztQiVoYmIiJKFYacFHL7AjGPhyYdcz4OERFR6jDkpFC8aseh4aqpXFlFRESUMgw5GRAOORyuIiIiShmGnDTrcXrR6/IBAKYUsieHiIgoVRhyUsjnjx6uCvXiVOTpodco090kIiKiSYMhJ4VOdDujjh3r4HwcIiKidMiakPPoo4+irq4OOp0O9fX1eO+994Y93+Px4J577kFNTQ20Wi2mTp2Kp556Kk2tTUxgyMRjSZI4H4eIiChNVJluAAC8+OKLuOuuu/Doo4/i/PPPx+OPP45Vq1Zh//79qK6ujvma6667Du3t7XjyyScxbdo0dHR0wO/3p7nlw4u1uOpYJ5ePExERpUNWhJyHHnoIt956K2677TYAwG9/+1u88cYb2LRpEzZu3Bh1/t/+9jds3boVx48fh8ViAQDU1tams8mjdqYnhyGHiIgolTI+XOX1erFr1y6sXLky4vjKlSuxffv2mK95/fXXsWTJEvziF79ARUUFZsyYgR/84Afo7++P+zkejwc2my3iK9XEkK4cr19Gc48LADC1mMNVREREqZTxnpyuri4EAgGUlJREHC8pKUFbW1vM1xw/fhzvv/8+dDodXn31VXR1deG73/0uenp64s7L2bhxIx544IExb/9wrP2+iMdtNjdkAZh0KhTlaNPaFiIioskm4z05IZIUuVGlECLqWIgsy5AkCc899xyWLVuGL3/5y3jooYfwzDPPxO3N2bBhA6xWa/irubl5zH+GoYbOyTndF2zb1KKcuD8bERERjY2M9+QUFhZCqVRG9dp0dHRE9e6ElJWVoaKiAmazOXxs9uzZEELg1KlTmD59etRrtFottNrM9p60Wd0AgCmFHKoiIiJKtYz35Gg0GtTX12PLli0Rx7ds2YIVK1bEfM3555+P06dPw+FwhI8dPnwYCoUClZWVKW3v2ehyeAAAFfn6DLeEiIho4st4yAGA9evX449//COeeuopHDhwAHfffTeampqwbt06AMGhpjVr1oTPv/7661FQUIC1a9di//792LZtG374wx/illtugV6fvQGix+kFAJSadRluCRER0cSX8eEqAFi9ejW6u7vx4IMPorW1FfPmzcPmzZtRU1MDAGhtbUVTU1P4/JycHGzZsgXf+973sGTJEhQUFOC6667DT3/600z9CCPSqhVweoN1fMoYcoiIiFJOEkPXOU8SNpsNZrMZVqsVubm5KfmMt/a3h7+/bE4J6v/vFnQ7vdh854WYU56azyQiIprIkrl/Z8Vw1WTg9gXQPTBcxZ4cIiKi1GPISQOVUkK7LbiySqdWIM+gznCLiIiIJj6GnDSYWWpC68Dy8TKznjVyiIiI0oAhJ0Vk+cxUJ4UkhWvklOZyqIqIiCgdGHJSZPBsbo1SgdPWYLVjzschIiJKD4acFBm8aM2sV5/pyWHIISIiSguGnBQZNFoFSUJ44jF7coiIiNKDISdFxKABK0mS0G4LbulQzDk5REREacGQkyKh0SrFwBUO9eSUMOQQERGlBUNOioRCjgQJsizQYQ/25JTkZnYndCIiosmCISdFwsNVEtDt9CIgC0gSUJjDkENERJQODDkpIp/JOOGhqgKjFmolLzkREVE68I6bIqEl5ApJQoc9NB+HvThERETpwpCTIqG1VcHl46H5OJx0TERElC4MOSki5OB/JUiDVlaxJ4eIiChdGHJSRA4PV53pySkysSeHiIgoXRhyUiQUciRJQo8zFHLYk0NERJQuDDkpElpdpZCAXqcPAGAxaDLYIiIiosmFISdFQqurlAoJPS4vACDfqM5kk4iIiCYVhpwUCdfJkST0OoMhx2JkTw4REVG6MOSkSGhODiDQG+rJ4XAVERFR2jDkpEgo5PR7A+FenTwDh6uIiIjShSEnReSBOjkOdwAAkKNVQatSZrBFREREkwtDToqEenLsnuDKKk46JiIiSi+GnBQJhRyH2w+Ay8eJiIjSjSEnRULzcOyeYMjJ58oqIiKitGLISZFQnRy7m4UAiYiIMoEhJ0XCPTkDw1W5es7JISIiSieGnBQJzclxeYOrqxhyiIiI0oshJ0XOhJyBnhydKpPNISIimnQYclIkVPDY6Qn25JgYcoiIiNKKISdFAgOTchyeUE8Oh6uIiIjSKWtCzqOPPoq6ujrodDrU19fjvffeS+h1H3zwAVQqFRYuXJjaBiYpNFzlHBiuMjHkEBERpVVWhJwXX3wRd911F+655x7s2bMHF154IVatWoWmpqZhX2e1WrFmzRpceumlaWpp4uTwcFUo5HC4ioiIKJ2yIuQ89NBDuPXWW3Hbbbdh9uzZ+O1vf4uqqips2rRp2Nd9+9vfxvXXX4/ly5enqaWJE0MqHjPkEBERpVfGQ47X68WuXbuwcuXKiOMrV67E9u3b477u6aefxrFjx3Dfffcl9Dkejwc2my3iK5VkAciygJNLyImIiDIi4yGnq6sLgUAAJSUlEcdLSkrQ1tYW8zVHjhzBj3/8Yzz33HNQqRLrIdm4cSPMZnP4q6qq6qzbPhxZCLj9gfBj9uQQERGlV8ZDTogkSRGPhRBRxwAgEAjg+uuvxwMPPIAZM2Yk/P4bNmyA1WoNfzU3N591m4cjC4H+gV4cjUoBrUqZ0s8jIiKiSBnvXigsLIRSqYzqteno6Ijq3QEAu92OnTt3Ys+ePbjjjjsAALIsQwgBlUqFN998E5dccknU67RaLbRabWp+iBiEGFTtmL04REREaZfxnhyNRoP6+nps2bIl4viWLVuwYsWKqPNzc3Oxd+9eNDQ0hL/WrVuHmTNnoqGhAeeee266mj4sWQj0+0Ihh/NxiIiI0i0ruhjWr1+PG2+8EUuWLMHy5cvxhz/8AU1NTVi3bh2A4FBTS0sLnn32WSgUCsybNy/i9cXFxdDpdFHHMykgi3BPDufjEBERpV9W3H1Xr16N7u5uPPjgg2htbcW8efOwefNm1NTUAABaW1tHrJmTbQQQ7snJYcghIiJKO0mECrpMMjabDWazGVarFbm5uWP+/u8c7MDbBzvw/z48icvnlOCJNUvG/DOIiIgmm2Tu3xmfkzNRCQh4BpaQGzRcWUVERJRuDDkpIgTg8csAGHKIiIgygSEnBYQQwZDjC4YcvZpzcoiIiNKNIScFQrOcvOzJISIiyhiGnBQKzcnRM+QQERGlHUNOCsgDXTmck0NERJQ5DDkpEFqTz+EqIiKizGHISYHQnJxQT45ew4nHRERE6caQkwICoeGqgTo5avbkEBERpRtDTgqEV1cFOFxFRESUKQw5KRSuk8OQQ0RElHYMOSkQvbqKc3KIiIjSjSEnhbi6ioiIKHMYclJAFsHenNCcHA5XERERpR9DTgoIIcK9OAB7coiIiDKBIScFBM7MxwEAnYohh4iIKN0YclJAiDPzcfRqJRQKKcMtIiIimnwYclJBDCoEyKEqIiKijGDISQEBMWhLB4YcIiKiTGDISQFZcPk4ERFRpjHkpIAQgptzEhERZRhDTgoEV1dxc04iIqJMYshJASEGb+nAkENERJQJDDkpIHCmGCAnHhMREWUGQ04qsCeHiIgo4xhyUiBiTg4nHhMREWUEQ04KRFQ8Zk8OERFRRjDkpIA8aINO7ltFRESUGQw5KSAA+GUBANCoeImJiIgygXfgFBBCwB9gyCEiIsok3oFTQAjAJweHq7QMOURERBnBO3CKsCeHiIgos7LmDvzoo4+irq4OOp0O9fX1eO+99+Ke+8orr+Dyyy9HUVERcnNzsXz5crzxxhtpbO3whAD8AfbkEBERZVJW3IFffPFF3HXXXbjnnnuwZ88eXHjhhVi1ahWamppinr9t2zZcfvnl2Lx5M3bt2oWLL74YV199Nfbs2ZPmlscmIM5MPFZmxSUmIiKadCQhhMh0I84991wsXrwYmzZtCh+bPXs2rrnmGmzcuDGh95g7dy5Wr16Ne++9N6HzbTYbzGYzrFYrcnNzR9XueBq7nFj79Mc40e3CkzctwaWzS8b0/YmIiCarZO7fGe9m8Hq92LVrF1auXBlxfOXKldi+fXtC7yHLMux2OywWS9xzPB4PbDZbxFeqCCHg45wcIiKijMr4HbirqwuBQAAlJZG9HSUlJWhra0voPX7961/D6XTiuuuui3vOxo0bYTabw19VVVVn1e7hBOvkBOfkcLiKiIgoM7LmDixJUsRjIUTUsVief/553H///XjxxRdRXFwc97wNGzbAarWGv5qbm8+6zfEEJx4He3K0alY8JiIiyoSM7x5ZWFgIpVIZ1WvT0dER1bsz1Isvvohbb70VL730Ei677LJhz9VqtdBqtWfd3sRw4jEREVGmZfwOrNFoUF9fjy1btkQc37JlC1asWBH3dc8//zxuvvlm/Md//AeuvPLKVDczKUIAvoEl5JyTQ0RElBkZ78kBgPXr1+PGG2/EkiVLsHz5cvzhD39AU1MT1q1bByA41NTS0oJnn30WQDDgrFmzBg8//DDOO++8cC+QXq+H2WzO2M8xWKgnh3VyiIiIMiMrQs7q1avR3d2NBx98EK2trZg3bx42b96MmpoaAEBra2tEzZzHH38cfr8ft99+O26//fbw8ZtuugnPPPNMupsfReBMMUD25BAREWVGVtTJyYRU1snZf9qGLz8SrNi85/+7HPlGzZi+PxER0WQ1rurkTETeQCD8PXtyiIiIMoN34BTw+uXw95yTQ0RElBm8A6dAqNqxQgJUXEJORESUEbwDp0CoJ0fNgENERJQxvAunQGhODkMOERFR5vAunAJef6ja8cjbUhAREVFqMOSkQHi4ipOOiYiIMoZ34RQIbenA4SoiIqLM4V04BUKrq1gjh4iIKHN4F06B0MRj7kBORESUObwLp4BvYOIxh6uIiIgyh3fhFPCFN+fk6ioiIqJMYchJAW944rEywy0hIiKavBhyUiDck8M6OURERBnDkJMCoTo5XF1FRESUObwLp4BPDlU85uUlIiLKFN6FU8DHnhwiIqKM4104Bc6sruLlJSIiyhTehVPAy5BDRESUcbwLp4B/YFsHtYKXl4iIKFN4F04BDlcRERFlHu/CKRDuyeHqKiIioozhXTgFQnNytOzJISIiyhjehVPAP1AnR82QQ0RElDG8C6eAP9STw+EqIiKijOFdOAV8AfbkEBERZRrvwingl7m6ioiIKNN4F06BUE8O964iIiLKHN6FU8DP1VVEREQZx7twCoTq5HC4ioiIKHN4F04BnxzqyVFmuCVERESTV9aEnEcffRR1dXXQ6XSor6/He++9N+z5W7duRX19PXQ6HaZMmYLHHnssTS0d2ZmeHCnDLSEiIpq8siLkvPjii7jrrrtwzz33YM+ePbjwwguxatUqNDU1xTy/sbERX/7yl3HhhRdiz549+MlPfoI777wTL7/8cppbHluo4rFOzZ4cIiKiTJGEECLTjTj33HOxePFibNq0KXxs9uzZuOaaa7Bx48ao83/0ox/h9ddfx4EDB8LH1q1bh08//RQ7duxI6DNtNhvMZjOsVityc3PP/ocYZOb/+Ss8fhlvrf8CphWbxvS9iYiIJrNk7t8Z78nxer3YtWsXVq5cGXF85cqV2L59e8zX7NixI+r8K664Ajt37oTP54v5Go/HA5vNFvGVCrIsw+sP9uQYNKqUfAYRERGNLOMhp6urC4FAACUlJRHHS0pK0NbWFvM1bW1tMc/3+/3o6uqK+ZqNGzfCbDaHv6qqqsbmBxhi58k+CABKhYQ8vToln0FEREQjy3jICZGkyEm6QoioYyOdH+t4yIYNG2C1WsNfzc3NZ9ni2KYX5+CuS6dj9ZJKGLTsySEiIsqUjN+FCwsLoVQqo3ptOjo6onprQkpLS2Oer1KpUFBQEPM1Wq0WWq12bBo9jHyjBnddPiPln0NERETDy3hPjkajQX19PbZs2RJxfMuWLVixYkXM1yxfvjzq/DfffBNLliyBWs0hIiIiIsqCkAMA69evxx//+Ec89dRTOHDgAO6++240NTVh3bp1AIJDTWvWrAmfv27dOpw8eRLr16/HgQMH8NRTT+HJJ5/ED37wg0z9CERERJRlMj5cBQCrV69Gd3c3HnzwQbS2tmLevHnYvHkzampqAACtra0RNXPq6uqwefNm3H333fj973+P8vJyPPLII7j22msz9SMQERFRlsmKOjmZkMo6OURERJQa46pODhEREVEqMOQQERHRhMSQQ0RERBMSQw4RERFNSAw5RERENCEx5BAREdGExJBDREREExJDDhEREU1IDDlEREQ0IWXFtg6ZECr0bLPZMtwSIiIiSlTovp3Ihg2TNuTY7XYAQFVVVYZbQkRERMmy2+0wm83DnjNp966SZRmnT5+GyWSCJElj+t42mw1VVVVobm7mvlgx8PoMj9dneLw+I+M1Gh6vz/Cy/foIIWC321FeXg6FYvhZN5O2J0ehUKCysjKln5Gbm5uVf0GyBa/P8Hh9hsfrMzJeo+Hx+gwvm6/PSD04IZx4TERERBMSQw4RERFNSAw5KaDVanHfffdBq9VmuilZiddneLw+w+P1GRmv0fB4fYY3ka7PpJ14TERERBMbe3KIiIhoQmLIISIiogmJIYeIiIgmJIYcIiIimpAYcsbYo48+irq6Ouh0OtTX1+O9997LdJNSYtu2bbj66qtRXl4OSZLw2muvRTwvhMD999+P8vJy6PV6XHTRRfj8888jzvF4PPje976HwsJCGI1GfOUrX8GpU6cizunt7cWNN94Is9kMs9mMG2+8EX19fSn+6c7exo0bsXTpUphMJhQXF+Oaa67BoUOHIs6ZzNdo06ZNOOecc8LFxpYvX46//vWv4ecn87WJZePGjZAkCXfddVf42GS+Rvfffz8kSYr4Ki0tDT8/ma9NSEtLC/7xH/8RBQUFMBgMWLhwIXbt2hV+ftJcI0Fj5oUXXhBqtVo88cQTYv/+/eL73/++MBqN4uTJk5lu2pjbvHmzuOeee8TLL78sAIhXX3014vmf//znwmQyiZdfflns3btXrF69WpSVlQmbzRY+Z926daKiokJs2bJF7N69W1x88cViwYIFwu/3h8/50pe+JObNmye2b98utm/fLubNmyeuuuqqdP2Yo3bFFVeIp59+Wuzbt080NDSIK6+8UlRXVwuHwxE+ZzJfo9dff138z//8jzh06JA4dOiQ+MlPfiLUarXYt2+fEGJyX5uhPv74Y1FbWyvOOecc8f3vfz98fDJfo/vuu0/MnTtXtLa2hr86OjrCz0/mayOEED09PaKmpkbcfPPN4qOPPhKNjY3irbfeEkePHg2fM1muEUPOGFq2bJlYt25dxLFZs2aJH//4xxlqUXoMDTmyLIvS0lLx85//PHzM7XYLs9ksHnvsMSGEEH19fUKtVosXXnghfE5LS4tQKBTib3/7mxBCiP379wsA4sMPPwyfs2PHDgFAHDx4MMU/1djq6OgQAMTWrVuFELxGseTn54s//vGPvDaD2O12MX36dLFlyxbxxS9+MRxyJvs1uu+++8SCBQtiPjfZr40QQvzoRz8SF1xwQdznJ9M14nDVGPF6vdi1axdWrlwZcXzlypXYvn17hlqVGY2NjWhra4u4FlqtFl/84hfD12LXrl3w+XwR55SXl2PevHnhc3bs2AGz2Yxzzz03fM55550Hs9k87q6p1WoFAFgsFgC8RoMFAgG88MILcDqdWL58Oa/NILfffjuuvPJKXHbZZRHHeY2AI0eOoLy8HHV1dfjGN76B48ePA+C1AYDXX38dS5Yswde//nUUFxdj0aJFeOKJJ8LPT6ZrxJAzRrq6uhAIBFBSUhJxvKSkBG1tbRlqVWaEft7hrkVbWxs0Gg3y8/OHPae4uDjq/YuLi8fVNRVCYP369bjgggswb948ALxGALB3717k5ORAq9Vi3bp1ePXVVzFnzhxemwEvvPACdu/ejY0bN0Y9N9mv0bnnnotnn30Wb7zxBp544gm0tbVhxYoV6O7unvTXBgCOHz+OTZs2Yfr06XjjjTewbt063HnnnXj22WcBTK6/P5N2F/JUkSQp4rEQIurYZDGaazH0nFjnj7drescdd+Czzz7D+++/H/XcZL5GM2fORENDA/r6+vDyyy/jpptuwtatW8PPT+Zr09zcjO9///t48803odPp4p43Wa/RqlWrwt/Pnz8fy5cvx9SpU/Hv//7vOO+88wBM3msDALIsY8mSJfjZz34GAFi0aBE+//xzbNq0CWvWrAmfNxmuEXtyxkhhYSGUSmVUeu3o6IhKyxNdaJXDcNeitLQUXq8Xvb29w57T3t4e9f6dnZ3j5pp+73vfw+uvv4533nkHlZWV4eO8RoBGo8G0adOwZMkSbNy4EQsWLMDDDz/Ma4PgUEFHRwfq6+uhUqmgUqmwdetWPPLII1CpVOH2T+ZrNJjRaMT8+fNx5MgR/v0BUFZWhjlz5kQcmz17NpqamgBMrn9/GHLGiEajQX19PbZs2RJxfMuWLVixYkWGWpUZdXV1KC0tjbgWXq8XW7duDV+L+vp6qNXqiHNaW1uxb9++8DnLly+H1WrFxx9/HD7no48+gtVqzfprKoTAHXfcgVdeeQVvv/026urqIp7nNYomhIDH4+G1AXDppZdi7969aGhoCH8tWbIEN9xwAxoaGjBlypRJf40G83g8OHDgAMrKyvj3B8D5558fVbLi8OHDqKmpATDJ/v1J5yzniS60hPzJJ58U+/fvF3fddZcwGo3ixIkTmW7amLPb7WLPnj1iz549AoB46KGHxJ49e8LL5X/+858Ls9ksXnnlFbF3717xzW9+M+byxMrKSvHWW2+J3bt3i0suuSTm8sRzzjlH7NixQ+zYsUPMnz8/q5YnxvOd73xHmM1m8e6770Ysc3W5XOFzJvM12rBhg9i2bZtobGwUn332mfjJT34iFAqFePPNN4UQk/vaxDN4dZUQk/sa/dM//ZN49913xfHjx8WHH34orrrqKmEymcL/1k7mayNEsOyASqUS//Iv/yKOHDkinnvuOWEwGMSf/vSn8DmT5Rox5Iyx3//+96KmpkZoNBqxePHi8JLhieadd94RAKK+brrpJiFEcInifffdJ0pLS4VWqxVf+MIXxN69eyPeo7+/X9xxxx3CYrEIvV4vrrrqKtHU1BRxTnd3t7jhhhuEyWQSJpNJ3HDDDaK3tzdNP+Xoxbo2AMTTTz8dPmcyX6Nbbrkl/P9JUVGRuPTSS8MBR4jJfW3iGRpyJvM1CtV0UavVory8XHzta18Tn3/+efj5yXxtQv7rv/5LzJs3T2i1WjFr1izxhz/8IeL5yXKNJCGEyEwfEhEREVHqcE4OERERTUgMOURERDQhMeQQERHRhMSQQ0RERBMSQw4RERFNSAw5RERENCEx5BAREdGExJBDREREExJDDhEREU1IDDlEREQ0ITHkEBER0YTEkENEREQT0v8PwG3UJbTw1+AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EMA_SPAN = 1000\n",
    "mis_smooth = pd.Series(mis).ewm(span=EMA_SPAN).mean()\n",
    "p1 = plt.plot(mis, alpha=0.3)[0]\n",
    "plt.plot(mis_smooth, c=p1.get_color())\n",
    "plt.ylabel(r'$I(\\mathcal{V}:\\mathcal{E})$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I(V:E) estimate is 1.4659861626437258.\n"
     ]
    }
   ],
   "source": [
    "I_VE = mis_smooth.to_numpy()[-1]\n",
    "\n",
    "print(f'I(V:E) estimate is {I_VE}.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimate $I(\\mathcal{H}:\\mathcal{E})$ for fixed $\\Lambda$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hs = test_pr \n",
    "\n",
    "L_E = Es.shape[1]\n",
    "input_shapes = [(hidden_dim,), (L_E, 1)]\n",
    "\n",
    "vbmi = VBMI(batch_size, input_shapes=input_shapes, layers=2, embed_dim=16, hidden_dim=16, activation='relu',\n",
    "            shuffle=100, learning_rate=5e-3, iterations=60, use_dropout=True, dropout_rate=0.4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f63a0eba0c34452ebb84f6ab9227a6e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mis = vbmi.InfoNCE(Hs, Es)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I(H:E) estimate is 1.4490246525001138.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EMA_SPAN = 1000\n",
    "mis_smooth = pd.Series(mis).ewm(span=EMA_SPAN).mean()\n",
    "p1 = plt.plot(mis, alpha=0.3)[0]\n",
    "plt.plot(mis_smooth, c=p1.get_color())\n",
    "plt.ylabel(r'$I(\\mathcal{V}:\\mathcal{E})$')\n",
    "\n",
    "I_HE = mis_smooth.to_numpy()[-1]\n",
    "\n",
    "if hidden_dim not in estimates_statistics.keys():\n",
    "    estimates_statistics[hidden_dim] = []\n",
    "estimates_statistics[hidden_dim].append(mis_smooth.to_numpy()[-10:])\n",
    "\n",
    "print(f'I(H:E) estimate is {I_HE}.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: [array([0.52419526, 0.52421819, 0.52424678, 0.52423722, 0.52424132,\n",
       "         0.5242537 , 0.52424395, 0.52426749, 0.52427928, 0.52429961])],\n",
       " 2: [array([1.08481872, 1.08487228, 1.08494154, 1.08495459, 1.084978  ,\n",
       "         1.08504265, 1.08509475, 1.08514318, 1.08522987, 1.08526093])],\n",
       " 3: [array([1.2567231 , 1.25679905, 1.25683311, 1.25686625, 1.25688898,\n",
       "         1.25692106, 1.25698589, 1.25696764, 1.2570106 , 1.25706471])],\n",
       " 4: [array([1.45102795, 1.45115756, 1.45120641, 1.45120294, 1.45125652,\n",
       "         1.45124823, 1.45132428, 1.45135268, 1.45141904, 1.45151285])],\n",
       " 5: [array([1.37884495, 1.37889276, 1.37887539, 1.37885553, 1.3788452 ,\n",
       "         1.37876548, 1.37882754, 1.37881877, 1.3788591 , 1.37892069]),\n",
       "  array([1.44935317, 1.44933332, 1.44938433, 1.449305  , 1.44918852,\n",
       "         1.44919627, 1.44904951, 1.44904599, 1.44902439, 1.44902465])]}"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimates_statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 98.4252x78.7402 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vals = [np.max(est)/np.log(2) for est in estimates_statistics.values()]\n",
    "devs = [[np.std(est) for est in estimates_statistics.values()], [0 for est in estimates_statistics.values()]]\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(2.5*cm, 2*cm))\n",
    "ax.stem(estimates_statistics.keys(), vals, markerfmt='k.', linefmt='lightgrey', basefmt=None, bottom=-1)\n",
    "ax.errorbar(estimates_statistics.keys(), vals, yerr=devs, fmt='.', capsize=0, color='k', ecolor='grey', elinewidth=1)\n",
    "ax.set_ylim(0,2.4)\n",
    "ax.set_xlim(0.5,5.5)\n",
    "ax.set_xlabel('number of bits')\n",
    "ax.set_ylabel(r'$I_\\Lambda(\\mathcal{H}:\\mathcal{E})$ (bits)')\n",
    "ax.set_xticks(list(estimates_statistics.keys()))\n",
    "ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.1f}'))\n",
    "\n",
    "plt.savefig('sigma2_8vertex_numbits.pdf', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "fff7285a7b9674a821da44b8413beb73bd11a21fb8e7ea8626d281a41e1eb1b4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}